{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 样式迁移\n",
    "\n",
    "如果你是一位摄影爱好者，也许接触过滤镜。它能改变照片的颜色样式，从而使风景照更加锐利或者令人像更加美白。但一个滤镜通常只能改变照片的某个方面。如果要照片达到理想中的样式，经常需要尝试大量不同的组合，其复杂程度不亚于模型调参。\n",
    "\n",
    "在本节中，我们将介绍如何使用卷积神经网络自动将某图像中的样式应用在另一图像之上，即样式迁移（style transfer）[1]。这里我们需要两张输入图像，一张是内容图像，另一张是样式图像，我们将使用神经网络修改内容图像使其在样式上接近样式图像。图9.12中的内容图像为本书作者在西雅图郊区的雷尼尔山国家公园（Mount Rainier National Park）拍摄的风景照，而样式图像则是一幅主题为秋天橡树的油画。最终输出的合成图像在保留了内容图像中物体主体形状的情况下应用了样式图像的油画笔触，同时也让整体颜色更加鲜艳。\n",
    "\n",
    "![输入内容图像和样式图像，输出样式迁移后的合成图像](../img/style-transfer.svg)\n",
    "\n",
    "## 方法\n",
    "\n",
    "图9.13用一个例子来阐述基于卷积神经网络的样式迁移方法。首先，我们初始化合成图像，例如将其初始化成内容图像。该合成图像是样式迁移过程中唯一需要更新的变量，即样式迁移所需迭代的模型参数。然后，我们选择一个预训练的卷积神经网络来抽取图像的特征，其中的模型参数在训练中无须更新。深度卷积神经网络凭借多个层逐级抽取图像的特征。我们可以选择其中某些层的输出作为内容特征或样式特征。以图9.13为例，这里选取的预训练的神经网络含有3个卷积层，其中第二层输出图像的内容特征，而第一层和第三层的输出被作为图像的样式特征。接下来，我们通过正向传播（实线箭头方向）计算样式迁移的损失函数，并通过反向传播（虚线箭头方向）迭代模型参数，即不断更新合成图像。样式迁移常用的损失函数由3部分组成：内容损失（content loss）使合成图像与内容图像在内容特征上接近，样式损失（style loss）令合成图像与样式图像在样式特征上接近，而总变差损失（total variation loss）则有助于减少合成图像中的噪点。最后，当模型训练结束时，我们输出样式迁移的模型参数，即得到最终的合成图像。\n",
    "\n",
    "![基于卷积神经网络的样式迁移。实线箭头和虚线箭头分别表示正向传播和反向传播](../img/neural-style.svg)\n",
    "\n",
    "下面，我们通过实验来进一步了解样式迁移的技术细节。实验需要用到一些导入的包或模块。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "1"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import d2lzh as d2l\n",
    "from mxnet import autograd, gluon, image, init, nd\n",
    "from mxnet.gluon import model_zoo, nn\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取内容图像和样式图像\n",
    "\n",
    "首先，我们分别读取内容图像和样式图像。从打印出的图像坐标轴可以看出，它们的尺寸并不一样。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "2"
    }
   },
   "outputs": [
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cHk4xswiqgla9h+dGSGOG6TaY5xFn9ve4e2/KD394m5sPBtiGw9ZqhQ7b5Dri8TwlkDbxvOLcuRpO4LPRb/DO3RBLCMq8wm+YrLYtcmLsZh8DC1e4NJySTtBidWcNqSSjRUkxu4NjwHB4ynpdUpp1wiTk7oMUYZZ4ZsqjmYUkJ69qHJ7MoKjRbAv63YC1dp3FaIbZukC7ucN8MeEzn/wci8WUx8eH4DlEy4Ru02I2mZMXilIt8NyA82dddp2I90cxalswGOQEYoHhWepVYSqCRoFXt3DdNnm+pMp8HBuKNEWVNtrQGEKgcoFnpbR8l7AoMQuIF5oyNYgzDSqn5ivCPCOvMrorBmFU0O+B0fLQQjNNClZtH1dZjOMFhVtQczy2t+qAQx66hMmcjTVJs9nl9MExP/GFz/P48Aa/8uu/zZ3piF/42b/ON9+9xvU3ZriqSZqWLAdwMs65ee8Bf+2v/Q2+++ab+IbGMwVukCOyjEka4MmMLNdceH6NtrnK4GiCUgueOqt55YnnaNZ9/u1XfgljFZI855lnz3L72jFZYXIyEUxPNIFr4vsQDj0+9kyDTsfizsOMta5gOCt4/VbI/RtTdi+dZzrJqHRJP/Dodl121zSLRcGdxwsM16HtV9g+DKeQJQvSqSCOZhw+nqO1xyTOieKKvPhAyhiySSYCFmlOVfi0u4o0V0xzk42+Yqaa9LsLnrwiORxrnr20hVPOyEuTzY5AWiWV2cItFqytn8E0MyzDw5UFzbqN4QdEaU6zUfDs3mVu3D6h1gY/WOfqpV2Ohw/Z2b+A79jEkWQyjBlOlqyt12g1XPb3V6h3VwnDgiqHwXJE4LU5mS/Y297k0WjGZBLykWcv8+5hyk6vyzTSjBclaVwxyR1EtmBztcPDQYQXNBmHc+I5NBsWK1srCNmiyg8Imj6xkBh2QhJN+PYP3uP+4QmtVoc8z7Fti3lYkmsTN5Cs+jWS6Yjnnu6wXEZUdY9mPeXmbVjZXsMI2sGrOjPRWkChKLKSUpsUqqSqNK16BlhIZVCJAsuQJLlBnoGQUKBxHAclA9JygTSg3faIlxqvUWFEKUkGpRDYoUueK0w7wwgyLM9mq7vLaD5mOiqo200G0xP6qxW2W5KbAbYQfPHzP8U/+8qvIlyDvFqwGGf89E/8BP/7r3wNw8xYxEuwSyhLNnc1ByPFJ64+xY0777G6umB7p8l0KjhaFpzb7mOQUhUpZ8+/zMGjG1y9IinKnGnmktojilHBV6/d4d//S3+VKrvBnfsLfLdFlhVYAhACVZboSiBEyp1HC/ZXd3nv0ZLFAp55OWBv5zI//IWAg6Mhthkymwq0FdFreTS7KYOBpFm32d5bZzg5pshj8qpHrQZJNOHc1gpGC4ajlCIr6fZclqFGKDA8g+WiYL7MePrSOnERY1cmHVcxK5dQKdY6LbpdzePTGAuoeS2W0RCr3eT8k7vUAwPPinFrTbLQwQ1aaA2ezql5DbZWXbSQRCVM4xC7ZzB+fIzZNvnkK+f4f9+6Q5oWCLtNpQyOjsdEy4StngmOQ7JM8S2HqijQps0s1dy8Oef6rQkPHy2pcs37dwYcTGCr6dA6+zSHD07odtuEs5j9Mwa9WsGDsY1jmtiWoFKCsoq5eGmFe/dPefHps6QJ3Ly9YDoNqbU84tSgbXnMopiy1Diei8gVstD0uyuUZs7zF6a8d3eBtwIvP+vw+g9yPvEJePftHobj2K9aZkk9MImSAkuWSMsgnGpU5VBrQJYIlFIYRkmaOhhuClJS6RghbPqdLst8iUoVoipBKLKipNcUiAJsV+MFgvlCEcUZLd9EVgqjVjE8rGgFLq02+G4JQpKXktOBTWDDhc3zvPnON9nfv8DjB3cpK5N54vDUFnzv3RtcOGMglcOV1S7LrCSgycms4u/+F/85v/Lb/4rTmUk41HhFTCp7xNMRrukBkq2a5O69CZc+9CTHx0tOTmB8fEyt32YpCr78o1/g2r032e/5BFIyDhPW1gOIIoTl4xsKYWniWPCJJzZ4wcoYNZfEI8Xf+dw6X71p8+g4oUpSEiUpY4GoMmrGOh968RKXmhmDcYg0ItKwwWCcoVVKp1VjMdcMTjOCwMOoSlbW1lhGBa5to4oSZIHvG5BV1Fwf20noNDVCm7RqFa7rYPgV0TKgXvfZ6tZoB31W220Wpws6zT5+u8k8CVE0yNMI4Ui04yBJOJnOybTm0vY6h8cDmq7HYjFnu71OHkOWmRiOzziJCOMJQpVsrwo2Vi0qo0Y9CJiVEYbjYQlwPYMLF3a5fXuKriq63SaL0CQuYubThKPxlHCRs9EAu96h2y15+96UKnPJqgrXtsniAscsiUpNUQgsYSKkx52HU6jAMDzSLCVJc2zHAcei5UjCOKJSFbP5CHLB4WnKS1clhqW59pbDzQOfBw8D8mSJEZjOq9pwSJKMmm9g2DVMrUgyG0MIHMOk0hWVKpCWgS4MlJnh1yQFgp3+LuPBIZa0gAoEYJVIo6JmghKKFJidCFZWLOIMiqzAdptIIyUrEnb26ty+t8C0bJ5bb1PVWiyWS/75L/wv/MIv/iLRPGUyO8WyFLbq0+sb3F2O2Vy3ePPNiJ6jGS6W3J/anEYRKIMndtt8/U+u40tAmJSWRc+HIksYLTJsWbB/foN37k75kQ/v8d4bB1jCYRYlGDcP+ZWv/Gt+5m/9LLHKkGrBmX2PLHYZDUZ0+xZWofAssE2TT3/sJX796+/w1nRBI4DdnTV+9Op5/sf/83skiyWW0SaZL4i0zxNX9nlwcofhYonRO0u4uIsle0jX5mias77qsLW2SkXBxmbAo0NNuyWYDU8RRkAYZ1RK4QculpVyca3F6UFIc93ByGbUmw2yakm4qNja2+T+YzAMTeBaDOc5iaqofMkyj1guKybTObZvEQtJFlcslgmmBZP5BLeUDOIpwjIoDZ+toMMbBzMeDxICo0IqqPsOduCw3q7xwnMXuXP/gLBa4eHDAefPrNL1JGEWY6qKJI64f7TAEjZZCYku8ZAsc41lehgUKMD2HWyzyehkQVhofF/iGAZhBqudDp1aD2H0uP7ggIcHC+yaRVoq0jjHsT4YwCgrcS2JKQwcA4SSOI6DZZr8ez9W47e/1WBy6qKsFQyhqKqKmmNiNDrmq7V6jBQuUZKj8pJIVQhAo4iLEjsoCAJIKpt2XyPTilKAK9egSMBIMQBbmmSqxDQUKtX4gSaQklSAEWjEwqaqVehCsYwLzuzXMMM+bqHQdsR4kNNd7XD9+mN8o+I3v/H7iADe/v2v8k/+t19lY7fLxSspRh5w63bOwb0Rz73og7aYDAs6dY/2asBykfAXPvs5fu+Pv81e3wEFH3ryMpZO6GycZa9dMJlm3H4w4BMf2+S3fust9p9dI8kGVGP4/Jk2/+uv/SarfQ9TFJxZv0CUG7z21ik/+hOrzJcGx+MC4pRz57c4e24DLx+i05R6u8+1Wwt+8P4Bh+OK9a2AWRkyWdS5em5BNBrx/HMGll3jtddvUmv6nC5LkmjKdCGxrZKb1484d97mvbeXLNOcuu2gioJFXmEKhTBACIt6XVJ3DDyrYr7IWNleo1uPkJbiUy+9iDJtwjxlY63NXCu0LShchzSXJEojTYll2xhaIm0DXUoCR7OIFEqbaKfOIvc4HggO7s5p988wOgxJFBwONZFhsNHt0LAU6x2fSgUEbZeO7bPIbWqBYIlgOChwu22GWYEWDlVekWUVqiiomQ5FWdLv2lSlRuXgNW0ePT4lqLksowzbtghaFjqvMAUcDpccD04whPzgLV8UFJmiXneYhwlBzUVVAs9zoag+yBjKilRXoGE8ahJmGeFSEcUJpnSoOy6GAYZlGq+WpaTXNIlji5U1Rbx0MI0C33dpdDTRTGO4BlKVpGmJShyEtrCNkIycMi0wmyV1IciocOsWFIBSHC8stFGRndrYdYdcxSgl2GiYPBgUjIs5647EbEnctsDVFkEtZRlXFJnAtm2+8q9+mVLZzBYhB3dzFnGOW5N011Lu31fYqc/P/chT/O7gARcuXebvf/nvcHLvHjdufp+jqKDh1Tg8PCSKY6LljNNhSCQUWQmt7hK7IamKGYYCFUnuSE29n7DXrPP3vvxl/uY/+jco6XF2P+GbfzjDrFcspoJmTVEOp3z//fscjGKyEt4ZRGzUK3q7JdOjnL/+332KB3dOOBlWOKXD0+cL/vj9FpOBYpkmrIicMJUkhYkqNM+9uEbdrJOpKVWc4td66DLFrTXxnBxZFHiex9ZOH7McEaopP/bR54iqAdKqmC5CnntyjduxzemgIqh3OToqyKIYLQIGRynTeMnD20smk4Ka7ZBngig2uH8Qo3QD13do+W2aQYum53PweIGsJO8PTuk0XFy3QqiKz37yKl3PZ6V1hqa3yTKeEiXQDNqc7a/wF196hXOr23z2pY+z3+tz+GDE49MFZViQ5gWOYRCXGaVS2IZFUeQI1+dLn/sZHo/vYYkUrTSGC+f7K+QYmFnE8VLTrrsYtqAqCnrtPlmR4TsWGgPLFCCg0XQRVYbrGiRljOfY1ByT00WEZZoUccLO/i7JMqHdDZhNphiNmni1yAPSPEVQEReCigpb2pRFwWwqqCqfIjNprAuSoUbaGa6rWC7BrEt8z2I51WRxne3NgjRXWKZHIjWubTOeFJzbbjGap4S54IufOcs4KWhu2my0fLYsm1m1wPcU00nEzUNFt2eQ25LhSUaZgNU0UVWFt2WyVhUgJE8+BcdHBsO54vrhgOWiZHR/xBuvf5W9nUu49VOmR4pzFxQvf+Iy779/SBZZKN/nw392k//gi6usNgveuy357GfO8Kd/PMbxWpgy5crzFvOo4p/+2mv85S+9xHGUcP2dIc8/61MlKeMRpGnFj/zF55lNDjkeeyjDZN0qGRWaaKYwTMF3XzvClbCIQoTM2G1d4NbpMVChFOydXydaTomMGslUo6oJruVRN3KOJj5SQK1uMjpdsLVpIlREVlYcn8Z8/OU6+22fei0hMytajQ7n9rd5/doj3rle4/DRktsPJ5Qi5wc3Z8SVJFzA8aMMVZhAwe72Gr/3nQOuPrFPq9nAb7kobVBWgkwrskJy5/4E4UqqMMWpKTTQqQX0+2tUQrFMIx7NRiilqXSNe0cDTpcxbz0+4NbBhD++9xa/9u/+hDQuWJYFRVFBqSlNje/Y1HwbQxc06zVc6TKpHtG22iRpxVp3ha7vkVWKVt0ljAVZaZBT4joWWhpUsiTLFK2GR6IkSRTRaLVo+D6W6yGyJYYwCZouaSIJmk00BfWgxunwGDsQhGFMzXUwHMt9tULTaBakicT0CizlkuSAYdDwCnRRsr2yyuFRiFtTVIlHWdgoqYgXGcLQmJWJHQgqUvqscpiNqPclq9rFMATjWUirI+l2TG48OEUMctr9DncejHh6o8ewmBPUm+ytmJRCEMUlKnXYXXE4WVTU3JJe4CAjSSJN2quKBzcLhDQRuSIrCvxGF8/L2N5TnCYha906Z86FfP+1kD+9dkzPh9Is8L2Uj17U/O4fHXAcpfzkX/1Zfv2X/gAl+/TamijM6fU6nN+oU4mc//v3Dxg+nICsCOMCJ6wzmOX89//NZ3jva29xPMkRbsUzT5/hdJGQVzZGpYlLg7VWycE05cKZdfq1DMsrqTViPv9Sg5v3JWcvtinjCfHCI9U5VZazt+FztFwwm+Y0V/s42ZhFpdnuZ+D1sdsRWWrSbCTYRsnhGC5esHn/JoTDEMMzuDcx8IIajitxrYI/89JlWo0mBpLJPKTXbnHx4ja2bbDWrZMWMVpVZEmEY2lMw6LruCRRTFhYnB4e0e55WIaNKzSpTnHcHghIU4dCSwoleTg8pd3aQEsTQ7hIaRHFiprtcXA655mrl1icTEhKTTcIMEWFbSl8v4Z0CmxpM1umPDg54nSasr3ZZrrIMJVgHhfcvD/EMgz8oIZWBXla4GpFUK+hzRxPOWil8DwDyorKMHl4suSJHZestHECwfRkRLtVb6MQAAAgAElEQVTVoeFYFEgcw2K+nON6FobrqlftQODJHNuEwDLIRYWJwnQ0UWKgSZmFH4RbJgZSSMpKUlUS0xJ0OyZxFSMcKFOb5zbXmCRzotMClRtc2FpjHE8JlSbQkBYGa70G9++dkHuaaJnw8otb3D045dzWDt/+7piddTgIC8JlRdCpyEKTZapISo1lFNhY1Nols0WdZRqDMEjjiGSuePaZTRw14xvfPuDokeDSXo+nzu3wyU+1uPbWnFc+tM7rbwuOJ5BkLtm11/jekWato8iSnK0tD1f7PPHUDoNFyMmwQBs+Smlkpbh0QVJkBa9/f4Do+NyfRvzPf2ufd44PCcOM6dSi7VsIVWE0OvQaETpc8nhocTpcUiqLEANbuhxNQ1wXDscW3Z7B2b11LH1IngvSoo1Z5WxuOEzmGetrHqORwZm9LnfuphhVTHPNIrAsvvOdU94fBDz/tMvv/8mc/c1NCpHRCFyyRDMMYywjw3EMRicxiA8u/O07Y+IyYW93l9EyJyvhdKJQVp07kwm33p2zTCN2+i0UBUob1B2b7a1NklzgN2vIMseqeVimTavbwZMOLc8hzTPCcIK0JJkVkGcl/ZbLcDghyTLcQKPyCsPUOIFHUqSsdVxwTJqBxyLMMU2H4WjONEqQpkuiNDXfRQpAVdiOS8ESrWxazTppVTHLIrr1OjmKtNSMZxHxckmRQ7vdIy6gNBTLKGUWLtEasrQEDUa7ab5qmyXSEszmgqIU2L4kiwtqDUmSQLvVYKXvkIcFjpSEhUJXAiUEtlFRZin9lRbpIicSFVd3ezy+N6ez1sMQS248ijjfcZhHJkVV8sozVzh8MEd4BXmoqCrJ5KBivZtiaLgzECwXiu0VRalg3YGga5JHFiIQzBcVvZUWXuVyeBBS81xMJEr6NGuK/+Of/k+8+OLn+Re//DusrNRR2ufhwZRvf++ArHS5fbBgmUiqrCJaZkwrF0Mreo6k0ap4dBCysVHnOz+YsNut8/p7cwwKqCrCtKRmuvz5L15BJwrDnfDKMz3uHEJa9bn5cMmzz0oubVnkhUu4yCi1x/Qk49lLPTI7x3ELXK/N2f02j0cxbdtkHsKlfYdkdszu2XV6rW1GUczOpmJeTRjMLT790XVuH0j6bcH9h4InzhrkSmOUmqNwE9/x2e7bvD+QYBoUcYQ0JELYzGMYDGPu3J1TFjmWYTJepshA8MTFfQbHI4SwOb+7i64AFJ7hcHI6Q5gmSsf4vkfgmghRsLq1Ssd1KQqLUts4UnO6TCjjjJVWjVmcUG/X0ArSrKLvKQbTGWttG1Ma5HHCc1fPMJwtadY8dAWB55Bok2WkOHgc4poG8zAijUMs6XAwGNOqBRi6IlyEdJodkihlpd+mqgSVqrDKit2ddYRvMJvlxElKw6vjWoJGzWcQF7RaDvEooVWvUWZgWIK6G5AXBVIKDyEdVlZWcWuSmuNhCwPHsxhNK3rtlOk85HS8wLQLFlmJVCaIHFsvWen51HtN4iyl0mDnPq/ffEB7xWZwcIwdmMQi47DIqPcUlS55893rpGZIlEnS3KJ0BP6KZOfsK/zOdxOKyqPeq/HoyKIMJJ4NIjdw6gmbez523eD0dIRsCVb7LvO0YFkoDCvHaoFeNPhPv/x3ySuXo8GSwfyAn/pPvoALxBnoHHSZUIqEZWkzj0KiMGcQ5rzxvmCWSN59fMBwccSv/t59IEIp6/+XkE3mWvBbX7vJ1TMbJMscsyr49PoejXpGsZBkWcaDazO++JdeITdgsZzj1Cz85pQnzuYcnro8OFpw49Y9tlabOI7ADRIsJ2D38lmu3xjTWV1Fp0PyaMpmEOBYLb79XYOap7j7YIGUEZcunuFjz/8Q4QyWww8M671FDxuTRZQTxgUGBqNwSqNWx9LgWhadXh8lBVLDSq9O0yt58sIWe7t1PHvGbtfgqTMdtlcDmoFECI1Xt5GUWFIj7Bo110LUKuqtAu3PUSJkrSFptRRR+QCrHpMkJ7Q6KU+cq/BrGo+EH375OXb2fayWoFXTbG500UIyVYJCFvQaHXa261iOTb/fo9cO0IZDki4xbJ/TyRTbtjm3u4ppaTordXSeYriQWQUb/QaOY1FFCe16m/PbLaI8pt5fpbm9STiZUaUmcVXxeDxBBjbNoMtsNqHMC4yGoV71AovxaUSUWgS+RuWCyiypNwTpBLqrDkIptGlQqQLLzRBoLGmTZxm+0aXujXjm6jM8unvIWrOPE9QYzlL6HYkRSERSUscgRyKMCpEJup2KWlsj0ahK8bt/dIRhmahSE0VzPEOx4Wq6KzaWVWeexoTTlHKqyUrJ0XGMbFsUk5Rmw6MWeJRhyUrfZ5w/YDzNKOcpaxsGX//aTTIhWe3VSEqwzZSzDQfDCqAsSUsLs57z517psMhCvvjjLzJ9FLO6FTCZRaz0O8yWEVpXfOLDK9y/lTIYPCaMFXlR56e/+AI/94vfoutAkdmctTQ3l6ec3bVYqUsGJxVmq0a2rBiENTqBYmdzjz/5zgmtuku7LthtN/j6d06YH8fsrNZY5AnhJOO555/n/qMZJ6MJm3vr7PRMBsMZRZKTLGd0egY3xg36jQ53bo0pZUrTcz6gAoySHIll+sg8p1ImizRG6BKvGeD7ktIqKaWFEhajWclcm7z13j26wqKgQlQmwvFJEoPTWLLab2ObAYNFRJy5hKlEVzkt6dG0JCr8AF8pM8HF/cu8f3vKdDSn1WnwG394nfXOBmiXk0FCzfHJ8pyVdh0jg1keMV8oqqTidDql5lhEUYpSCsswqPkO53b62LLErgd4pol0LBwkvl/Hq9eYTKa4XsBkMqKQCqEkrlUxOjil2fQoEQhZYqQZrpriyCEmEte1Mdb74tUk1dQbPoYI8b2KMFNkoYEWAlGYzMOMekfgmhKhNKHWbPRNyhKSwsAwmyzSmMeDIftru7w/GBKWIdJP8U2XwUFMKU2MSpFWJf02ZBjYumI80dSlxUmWsbIRsJjkeIHF5nqL0UnE5Ssei8rl1u0pH37mLMenEdMUpGHQrDs0GjaLQUpU5hhS0erkXL91i8loyNUrgu12xd6+z+OTnP3+KvvrS46PYv7Gl3+G3/vG94izkjAS7O3Vee5Z+OKP/3nS0wOOUzgaVRRZwOrmDvcfDBDa46984Tx/8NpNPve8x9MXXb70kz0O7h3zmRc/zIXtbd64ecR8tGT/ifP84RsHBJbPgwczzm1Lovs5L760zs2HBY4jSYsJn3hmm7TSPHnpScbzRxwfzwlch2C1h9tZxyzGmIHPO7cm9O2Ig6nLw6XgwqrPR166wjffGtESI+48bmBaGd21LlG2oNfoovOY9fU+jiO5c29B4LrEixyvHeD7HrYUXLjUZLW5QVwskTGkoqDumTQ6bR4tQ8htpHQ4PJxQa7psrfSIlxNe2Nzk4xcu8pFLZ3CzGVdWN4h0RoFkMlzw7JPPs7ifsa89zmPw1mFIrFbY3rpEGis+9aGPUN27y5OrfXabPZLhhHvTnBf3n6Hl1LGsjCxXXOxaaGHiBTabjRqrvTq7tsVH9ve4OZsxHi3odX18v06lNLdvH1BzXEZJhuvYdO0as2XCxfNXWISn+K5DUkDfb7PRekgryBkPM5q1DNcpMJq12quGmVAKmyovMbIKYUFRVsi2zUeffJlbD8bEC4swNFnkMQ1pE8UVjXpBFJs4vo2IE0AxWs4JPBdRJh+06OKMj1zd4NHhkjS36PQrajYkuYXQBpVV4YmAKsnoBgHLUIJQGGrB5StNWqogFBbDMGV/K2bFdzmYlzgVSDen2fFYnhTsB5q1HZvNM6s49SZXn65QpclQu/zpO0vWGi0s84SVZ0zefVRy7Vs3cdoFT3+oy2m0ZHsDjsKEmjjm3eMRw5MNksgjVy7j8Sk6Tmm4DieTB1w6C0mc8qNPf5rfeu0t4kzyy//6Gq/duck8juluwP5ZEJZLZGg23YyjheT2XHLen/LiMx6PFhbbDYnd7jAeTWjUfWaDI55/8Sl29rYIVYNprAgcxe1HY6rK5Me/9Ax/8vaS/b0OLz/lceskod/dprV9kfsPBpQaojzGNSRuUENYJYicbsvn0eEM4fqMF3NsYHXNoN7q0WvVmMdL2k6dUqYEfp1xXNF2G9iGprXaYnByyr3jKVcudBG2wLB9hkryxv3H/OH373JtNOb9k4yr53+I/sYGy0XE45NjTMfkmRc+xnDjEkfRggKfM+vrjEYhL3gps/EQLVJkmnG5LlkJHJq+5mI1ZWPrLFE85gXPYWavctXJeHZzg+vTIT/sSN7OBVoatAFqq5jFksv9Xd49GJLlGS9f/QSGUmxtXmGZjf8/pt47WLL8qvP8/O7v2vTm+Xqv3iv3ynZXd1V3tXdqJyHTLYkREpJWw0gIswtiIWaDgdmZiglmYpgNYoNYWHZAgBhCIIwQSC3k1V7qUpvq7ury5nmfPm9ef+9v/8iSmIzIyLw284/zvef7Pef8zuHYkRM88f5HWO5qWEmXe4++yqCVogwNSUapCDNHRpFzc7nTWRKzf3+Rbt9lpLaLMIqxHcGuMjS3fHpuC4WOigOqYxqDPphOgl226bs6adwlSAQq0YhCxUQ5ZSTv0OuG2NJm194CG2sdDFtjemqGQR+EmeEGIbMFjS0/QjcV3XSY/9A0mKzrxKHGrj2Kbujg5D0Ozh3FybnMTRZ581qPUg3SrYReYtH2Yg4d3M0rP1wFL+XqjRCtUuHKD3eoWA7lvElpNGY8n9G+qpOv6Dz+oVO8/P1L3H5PmddecXnw3hJvvr0XJaZxBzqanpLFHXSl03e7eIkgL0MOHYBSTfLMt5d4brHCqNPni//xk/y//3iOTCiiLpjSZGk94NSuAj/3gZO8vbpKvxUzul/ix3Uy36JajEGaaCWHc2/2OHyshmfU2XQN+nFGfbTAaL6Knzjk7DxBs4MbKw4ereOKInEK516/zvLagHzBJBp4BHHMrtERusEA1Q8wqiblUoVGU2FrgiiIyeUF8/tmCZKYJNJwM0E3iIgTnV6/h1Aab7xzkUfuu5fV1QZpZtILE1ScMBgIBqGDgc7iahsVZVixItQUr1w5y+r6AlHskzMcjLzNC6+/Qatxg7jp4tgOi6ur7KlqLDZd3vXY+9g7d4jj993L/KkHmDpwhNvve4jdJx9g+tAhLjeW+Mxn/z13Nr7MVPsa0U6PUznFPQcdZqImb4ST7Jk9xOWL59DDgIbnkTmCWq7EwrVVPvOZX2JzYxtMSTpQbKx0IFRMj+5jqXGUuUPvQ1O72HPrNWq7yiysgQw8cboy6tDfGdBqGqQqRtd0vEFMs5UQJj0Mq4gXaWiaoFpWpLqiUrRZ38wwzYiiaWPaJQ6M2izFPlU34e47T9Hux/QSwfrVJt3YxNQVO/0QQR+VmsRBhl2dh6jH5naKracEfkC9rlGvORw/eoTQbxInVVQ8Q6Hg0t5QdLox692UPbMpyzcgDSIsCbURlyBOeOSeHG4v4e1zbcZmqkyW+iwvJ6Rpwu7dI1gjio2mB0GO7saA5jWXIITBRohRzWNbgk4jod8LsG0Lt9NDCSgaGrVqjfHpPMX8SYIs5M59fZ57OeLL33uTsXqVDJt+L2Vry2eiVuadzR3W20v8q/feBZ1V8pUcF1dhrlykYFh09XEm6mMof4PK5AhBUEfZOoVKHhkn3Njqs933KVQqdLyA2kwZNJMklShNY2WjjZ0osiTFjyIKQieUMSLKKOZ0TKPAynqfTi/EFmCVTIp5i8s3NtHsMktrHUYqZQI3pVotUbUNYqVTLJc5f2OZGyttRAax7yOE5JaD87x4OaHsDOg1+vhZhFMt4gclyjkdv5ExOb2L8V3TjFWnOTJ3kMPTh3nz7Te5bXKEgu5SSAK8/CTVSsa3XjzDVL7H1ve+zvidx5kppPzFf/pd7rx1lIuXF/iHP/k8j+wS7Nm/i0qpxGurPgXZpVrWeM9MSi7Y4d2f+jUaV67wU+9+iiOzh9k/M0eYz7NyY4WeN0AIk7GxUcKBj56zyWKdnAmbrQZpJAj9k2ytj9Pv2MjJcU7n8jkW1xIEGn6Q4UUxpiOQkY5RNDEMl8CzEKSQKWJSvI5OtZQQuwI3URTLRbKwx6O7JS91QiqZw1urmxScGN0JEMBoXRCrDNVOsWoZSupkkYmWtDDygn4fsCRhOyLVDEpOHt2UuN4Y+tQu3nrxFerVfWz3uujFkJWzigyNfM7htz/1HgIVUd1V5dyVNm9cDTl1TxF3I2P37D7qfsLPfvYAb1y6wVvnI+46uIfXXr9KLksZZA5+L6F2RDA/OsBIGxycKCCTTS5eXUdzIsJAcmBihFZjjc/+xl1896U3+O0njvNH3/aZPzrLH/6b27kY5mlfvYoQJu+twt0fO0z/xjqjAxjoA07deZDFpuLWPXeytX2B+RMP0pYVIqFjjM8jUoFwxjAtHT+ISSMdpKDT6DFSqtANXKq1MbquT3fHpz45wfrSKrZuMl4fYffeOcoWzO6dxe0GjI+MstVoEQuLIEgo5Q0Klo0iJvAMttwe83MTKF3gBRnrrT5BX7DR6WFFgpGREpXROr3UYqRoYZYNdhoeU3XFZ97/JC9fuExJmORtSWoqTAXf/cYz/NZ3XNbXmrxy7gJLV95i6olPcGnxJVaWN8jHEeWxGWampynkCkgKdFyHbnUvl1e3OXulR3HuAK8vpJQqc9Sn5zkiL3D90g2mxmvM78ph5gzsfAkRRbjOOH/53QU6213K5RJRIjh/fYXEyDMIE0gj8qZJ4PkEaZea9BD6Jkm4TMtfZW3rBhtr1+gMNlleuYosl83TW2sxRdshTBJsx6Bol9g7OUnqSLY2W5iOThpbKARWUSeKMmbHdQyRw48SpKahZybZoM31WCP2LWqlMn7os9MdYOuKej6j29Ugr7F3b5WFrQFRP2XgDzOhmRyuwyjoGSIHTl3h2EeIYp+OOkQSWRyaMej6AZvXVrj/roNcv9ohIeMznz3Eq+deYLkvWL4a88R7TrFn/xxrK5cJmzp33Ho7r26+xS/9yif4zg9yjDiTvPr6KnEiCbH5D7/xaa5cPY/v5rDYS78yT1POsdPzaey0SUMwdQMhIAoDrpxfp1R0efGfFzhwz35ee+U8WSB54dVrpDqUjJhXuykdY52x6u1kocunf+03+cdnN8lVxrm+1qM0M8GqO4Kycgi9NCwNN/OstDssXOuxuNFn4Me0Gi66ZpIrS8qlMlqaoet5zJxFe6uJY1pkUYSuGRhajFF06EQRaRizHbhYRYu+l1DI55geL9Pre+QcHc+DI/Nj1KoFllba3HPbPnZVNIQjmJ8ao9PvU6vmSGOP3eWMxes7TE8VOTK9lyjSOPfOBRQmY3mTuZkq9916ikajyw9ffJ2HZhze2d4m7bZpxjtc/9GztAnZmxvlzsk8g1TiFGromsV42WHXZGW4REBKaoZE83tYBJS0hFIcsLbnMY7sOUzaukivH1IqmESBx1e2Jrmo5ohLOQ7O70UoDV3TuNQ8Q6RW2d5cgVybRLVoeSuotMHOziaJTAiSjDiN2D25m4YXMlYwSNMOUtOs07WCQ+dmJjCNFUmSsdlsQzIgX1aEuiINUrQYHEuik5AIQbPloRtQLtgkfZ92pjCCGEeP8QOPw4cmaLebiILGZkdR1EvYZkiSplTHNApGRq5sowUBgTQgSQldjY6n2DcjmT/yGDvNBVJjFtt0KJUaGHIPg8E2qWwj0mGjg7zjsHhNcWD3fjIJa5HL1sYl9k3Vufvucb71/YtM763QXHudqF/llZfOcvc9t7C8sEoiBM8+dwZHDwiTCjeWFji6uw2Vh7l09tvceuskes7Ca7VxOwPmdtV54P4Sy+s6ecdn92yP8qhNq2ljVzZIOwZFJ6WdahyY2E/Pd9lURYx4hwvnnmMt2Ut1vIbQq7Q6PjVbJ18uIlRAJ87Q05CJcQPVi+g0u+w/MIFRLZKFKa+dX6XXzAj9GD/LGPRdTCHJ5SxSQkZqFXQrD75PP/JBS7HLFbruAF1alBwDdEESaex0BsxM15kcy1GsFMlUn8gPqOdNLBUwWXNwNIWeK5K3TBorHUaqeTB1do8eYGJ8L5XCOP/6g09z9do1tlsL7Nl3K3bBIkoUdZmxe3ISLdVobG/xwO2PcHH9OscqNquxYHZ8hKTXJ4hDWr0+msjo9AZkaUYSeCRJgtAN0iwhX6rTqewnfuefmD1yN3rc5VrL5oZzkJFqnqKukMIglFCsObyxtkCrmbDV6CFSi0xPKKDhZRluqmg0PWzbJEsyOl6f0M+oyw3G6ymyVslOV8s2PVeQZhkIgVIKhYYXg+7rHN09zdZmi9ERHZX5pEqjNqehZRGWBWGQMohipiopfQmx7VA1Smx0IsAlZxgU7BRbBNx36kk2NpZQEeQmP8a+2QNcWH0HO9SY3jdLsZ5Rn4RryyHjE3dRsUuYpVGMMGR2fJypmsHZCxcZGysRh5LNnYBrK31soXHLycNsbq9x98OfI+8co9c4y/pqg8/9xqO88M2zpFqBfq/G5dVttnaWaZHnWAnkuGD3nlEkPu/+YI3KzARSHeLeB5/kzbPf5tb5/ehmSKEwylwtz9WFDpOTLgMfTpgGb21NsHxlhUsLCWNZwhubipwFXsdlVyHmybld/CA9waFb72DDlWgyj1YrkiQ6+WIBPUnohV0sPc/oSI4scVnbbOBoRfbsLeNYGRWnRKVaJuxHBL7PWN2kPuJQr5SpVMboeh1aviSMXOqTVcZHqpQsi3rBZLZWZmpqlLcvrmNqJmmso9s6Jw+NommCGjYly0YJQUG36fjDCtxQDUtZ4sgnX3AYHXWwDAsk/Pd/+Dpu1uHMdo9n3UlOHbmPA2NllBdTKufIGXlWmx5TVYutjRsofZSu36a7tcX+Q7cQZhnlyjia7tBPPDSZR2oOUegT5hyk7mDrgnyliBsF5Ksz1A8/zf5skdgyeDn3AFIMy7tN00JmCsKMP3r2q4RugkDiOA4oxdpOyPbmgCiQ9P0EhU67GbO60qPR8tlXb3PXw4fZ6a8jC0VOh3FI3wNNSADUzbctdCJNsLzjYWUaceQTJArSPI5d4afeVePiYkqnl2EaDshhl47tbZ96voxhb+BTJep4eFqB2049xosXvomVlNCEZPLI/Xh+k876Am4qyRUU9VKO3fVJRmcOo4ldtBTomExPjlAqakirxrlXvkt7K2Jhuc94NUcuzRgt2Ow5fgIx6GLO3o8hbWrGJQadHp///DlmxlJO3TfH2sIaC8s9arkKE8U87X7IbbvK/OjNddyewIun6bV34/XWMdKQj3z8c6wHedY2zjDqGKxc2+Rd7/kk5y9dIk4CZm4v0I99PvfYSV64uMiyZzI3ZZHXJIceEOTLeVbMg1hjM1hCUbALVEZsHENiOIJCPk+xWMS0TRxThzCk2euSK5XohSmGrSMVJBaYTo6ttTamZeOYCj2F8wt9tpt9mt2YO27fw0TJwpYmbhZjGRIr0cDWqeRNGjs+hw/OY+g6OcOmMFlACkXgWLx5pQkiIdEUUoCBIkVhFgq03TwL1xusbwx4+skPMzGxj5++8y52H72DW6aqvG9Pmd7GMmcuL6H8mKWtFo1uiBSCNIWd7jIjYzVU0EYfGePo5DRhqpBSECfDAk0tjUAldMMQ2yoSZiGa6RB0Bqg0pDIyS68wSnFwhi9dMAgRBEGIGycYiQJpEGQRK70GOzsdBkGMLTUGvk9Ot3AjH12AaZpkkSLLEoI4IolS2s08r11os3RVIXMFeVqTNklsgVL85CUyNKmh0gwlUqZnDHQjTxRqgKLd8Hjzchc9THGKGmYeLELCNESjQOB69AcC04iIBjq7Yp+ZoktRuVRrRfKVOi9985toLHD4lgfZvbuM6jY5fPQUudG7qe26m4HfxtEkSkvQ04h2AIvtVebyHlq9RCba5EvjBJ5HN4458dDHWWsP2PYucWza5vWz/8DVxZA7b5/kf7ljhj/9epM9d7yfURrMs031+BwnnzzOd79xjk4wrFNa3m5QzxfIT+/BDaokSZ8o8Hngrg8zPnWcH/3oOa7fOEPbD3no1lv51guLvHPW55Z907y2sUHiJTxwok4rzXPvwbso5PYj953Ctk26cYy0FGasEKGgWqhj5izSVMN3PbphShyHmEonTgMKVo6RUZtARdSKIyRCgLKJYo8UiVW36XYjtlot9s2MsK9iE/oRnUEXOxNIaSENieFYBIMB690BU+MV2t0e/dhlulqCRJBpgp7XZVc1h9Qlxdjiv/z5P3Hq+J2sdou8eiPls5/5KCcP3YLnBbSTgMvrDd66tES/E3B5oUEnyACFJQMG2SZxtILGCrrWwct80thjtFDj4voiG90mK61rvHljie5gh9XtGxwv2Pz1D39Ib9Blrb1JFCSURw/hFKv4maA4MovvdfjO2R08lRFGKZZmEIYBQmhEqUc3lbxz9QaGNNFUiu3kcFseURxBpDBkyKATkMQhaZygKUWSZYRZhCpOILIMMVa2lJkLqVRHWV4OQGQILUFPBJZhIiKX6bEKdcdkoe3R7g9IDI27b9vD62evs3/OYHOQEiGQWcT0dI7FKwFh32F8zGNuqsy5t3sYAqYmBKtZyj2Hj5EZMc+/co1cN6W6a5rqnkmuX36DzJJ8/BO/j8jVIfO59PaPEGZGv7nN5IFTRIGPsflV5k98lL//0v/N3O7DvPTiy9RsDdfL+Oiv/DGB5rFx/t+zdKlJJPLcfWiWji9otJbQtAKF0Q4n73yI3/tP36K+e5z2dpOcIfnEZ+ZZXrzA6Ogora7HbNFhy3qcnAtf+Ye/5eDJSUadPgePnsQUz/ON5yRpJLh03mO0msfvDBidhmIdTtWqrBUnoLfC7CP/F3lb0Q47dNpNXLfHRHmEVCqEtCiYBr4XEwU9NrY2GHgt4kQw8CMCT6JijdtvrRFkil7bZ6fVYbpeIFMeFx22HvoAACAASURBVC8EOKMO41WL6qiJSgJUpmNIHV1oCAwCUrI4oWRaSN3GTzOaOy3KNYss1mj34Td++79y+jd/hf79/ycnL53lSRmyc3uJ7kCxtHyDjVZGrZjDw0f0A8rVhJgtvDAiZ5nkpKDpB1TLRXwvJkgSdjZCpB6z3XbxBwG5XI7Z0TmWm1sY+TZjWpVtBStXt9i1q0aEIB4MCPsJoanYY1XoKg/DcBj0W0g9JiGmvR2SqJSSVaQdx+QEREIgKjNEoUIEW2T9BkJKMPKoZECWKCplC7cXYxcsgoGPtAyCWCKkRmzUkcpHGo48rVkp/XYMyuDUyTmSxOe+R27h+pvX2bOvRqXn4cUxzkQeZJFCXhG4mxyeGufUPSaDnsZ2wyfow0zNQq84aMKin2R4fRetWKCextRmNXpeRqgMqrLG4UKbddPBzXr0GxuM5BL2jFWp7n2YkVKd7aBJ3Za4no8SOqXybgp2iepIlZ1oiij2ib0m3W4fNejjJinbV7/P8Xf9DJXJD3HHQw+S177HytUtJi2Xh9/zWb7yz99laSHhxeeWCZXOpz55jHZriWPHR9mTf5R1sU0QfYiOup2N/m3YVp5vf+0v0UTA5kKXWsFk/OjnkMVHeOmFZ6jnYj73yUMk/Q32HoD9e+D865ATkvrdn8aeeT95J4/refTcHiIWlO0CgzijVq3T7HbI6wV0y8Yb9Bg0dkgTDT9MOXd5AzPSqY9VKJYKCCnJCUGpbBFlGdLM0W13OHBwD/miRprEkOnkjByWXcCSNkmUgKXTSWMs28DTQu45eJJH9h1ifnyO4/sOcuf8IdaXlzhycB9HVZN+vM3rQZ+tls+gv06jdx0ZL9LoX8KUfVbCSwS4SF1gWRZR7JEInSiJiaVOJnR8zaHT6HNjeRUvUlh2HkeYbPW6mErh+TbX2i2SXszknt0srjZoN1vkNIOe5pPDpFSsE3gBg2wASpHGPlGgyFKDUqVAmgRkQQy2RqCKeIlOECq0zKVgawRphijuRiZ9VBISJRl2rkSGiZ/ESFOiGWX8VCdJdJIMZL4gTud0SEOHOE3Z3OoQRRpXLqwizDx9P2Y9ADfRMGVM2w0JgoRThys8dvcI3/zeCn6aEIQaaaJwFSSDAQO/gqVAjSnGtTGWvQ59X3Dv3Q+wkxkcmX+QrcotaK0LBHHMIDHodmPcJEJ1XmX2tp9FGhqGqSjn62j2GKFpYBcLVCpTZNLBCK/z5gvfo1QZJVVQLTi4nkuhf5Yzz/8ttb2H+eHXn2XQzPjI7/w90v8zLm5s85Gff4Su6lObzDg0fxSrvEoambx+I2P34cfRybG13SNzHGLN4djtj1EwzlK77ae58NrbIM7j2rcyf+IjVGb/FX/71Rf50Kky+oFfpuuPcG1hmetbHtfefo2H3v8xokwhdIWZL1JxCmA5ZFmG0HUcw0bTTXpxSKvTZGGzSW9HY6c5ACT5Up5D87NYJYUVJuQqFkIoqvkCuyYmGZuuM1rSKVglpBJkkSIiRUUxjpNhmA5FpVMcFPhW6zD+cosTh+8kcWboBCavXNviB2+tsLnV48LCNa5fexWVNjC0JobYIFdK0LWYQTQgEzoJgqnCKJphYRoFHKOIKywa3Rg/UuipidQkOTNHe6ePkBpRlKFpOjlNEgpFt9MjSCJytoNj5FAio9ftoAmTYtkmDDNsAVGnTblg0HA1vF6TSCuRhCFuZjEIY1wsbLtAO80TxzppIBBaSqzlsaWgF5WIE0BlKD1FmgW6cQE/sUi0EmGUJ0olmZCgCTQlkCN1eVolGsUCuJGObQgKMkLLx4wYJm035I7j85QmFXnVQpkWjjS4dHXApY0NDhyZZW27RbupDcsHUkXqpeR3maRuiGiGrA5ccvkUQxbpLF2hJHWM2XsZG5lk78H7ObJvN/XsKhc2PESmocwS77z81yyvnuHI/CN0Aoc0l6NgmmhZSLVishOl/Og7/4NjdxzhjR+d4/5j85y/vsznPvpB9r3737L/1seYnpFsdi4OM+vN1zjx0Kf4wn//Fgvn+/QaMU889BRf+OK3EZt9BnFEv7nIfQfuwbZNLu24iDijUjBR0sYLrnD2he9TFgm79RbZ5P1IR4fF/8LOwgaLN2K04BWe/eEVqk6Rthvxs7/8CxRUSFeT5GwH27axLYdyoYiwTfK2QavRwJCSzPcoGgJdC2jutOmlGZZKEYbF3P5xQm+AyBkIwyJKQJkOIQmONDFsi77XIs1irLKNoQmKeYtcJCAQVGfu4Bd/5Zf44GOn6Gx3uXh5gbcunWXlxqsgr5GqJdAapFmTLI0IHI0sS3ET2O506PsRfTRiTKTl4IcpS5sBl663WNuJCHyNfmLQ6US02hHSN1jpZxQMyfh4mW5nQLGYI2fl0fWUnWYTU2bk8jpxptHc2saQJqgYt9+n1e4jkoBIKrY2t9mmTkZKP8yRoOOnOSK9QJLl8TOLLJXDKJBIEUJDE+AlOqhhJXUic2iaIlYmKnOAbCiT0UAoUAJ0iUIhC2VOj5ZzbGzH6FqMSiSDFPzQwlYa73/qIVy1Sre1Q9eNaWyE7J87xPiUz8TegH1zkuvXA0plnUik9DsZhUqO1O/SHcScOCywHEUYKnYVMtqZxVqjxZ6qhVPYTWBCToOmKyjkXWbH8ly8vMHmto8KW1zd+QaPPPzzuK5HTjdJRIqQESXT4ZEn34s92EYT5/jA059mz8w0b137J4w99+EUJmgPSrz0rb+jkAs5+8oWsnScV8+cwcklZF3B1XfOcfDYbr74qQf4vS9eRSUOidbHnDrE5kaTUrVCFEXkCyFW/iRB6zyzY6OU9z2Ort5hvBpz4ezzrC7GfObRPWi1DIh57Z02dkHw4MlxLHWVL/3uH/GpB27FKxyg5/XRDAtdQbe5jZYqbFNnvb3G6uoN/DigMFJla6vNA/cdI0wF+VKMpSuKhRxJKojDiCgMSFKdnu/R7LRI45QoCgj9mKJyePLuDzB34AQvL+pEepEXnnuOM89/DRVdoVDsY5sNGrHHII7Z6oa0g5i2OyDSBF03YWXT5frCDlkiaTVjVFqhvdpmazWg6WV0vBjDMnE9lyiKUEGAFIKpyRrNro+bBByYm6U16CLSENOwkUKw1WhiaTqmYw9r3wZdeoOILOwTZRIv0bCEh7RzbHsjlByFK0ukqYnKIFbyZvBHQJZClkGWIaQAlUKaDY8NVzkhpESlCWlmkqXGcOgPGophIwKEBpoGKgOVIS3U6UEEaSxJlIFSGQYamYAkTTl/ZYmTe46wcaODqRXZewC2Gj7vXGny3gMFLi04XF93sWVKtQyRZpE1PfQ85EOdoyf3stlp4uTLdJoevi+xRyVLi1c4fv+HcXe6FK0xSnuPse+299NYu4DbWccsmiTSYm50lFtufy8Nd4C081TzDiO5PFLe4Ad//8vsn/d4+41tLl1YZHXwQx556FHIPUk7SalqA951zx1ovRs8+b/+KfnqXh586inq9Tq1/SdZu/oG+tYWVzc2udGJKFqKux97CgOH9iDiwHQZISUThRK+EXL3A8f47jM/YGziOubUz+AUj2Aao7QbF/nUUx/k7771Auc3M27Zk2EbirZa5tkvX+eTH6uR7n6Ktl/ELkiUyEjjlDBLIA1JtIzQ6xF6EUGUIQslTFdnbMKgYEm2G30unm/x9vl1VrYD4ihmc6nJWNUiiHqkoY+pabS6CW+/uoYxeQ/ndkKur9xA753DyJbYDpYIkxbNQcBKs8+FGy7XL7fo9AL8WNHfjNhsJeihw+pSmwmrStcbYCUmWk6n0e5QqpRoewNswwRNIuOUfCaI05g0ioGYYrlIr+/he110R3L+wjqTIwXcJKPT7jI6qdNqdnFdj3azRxzF9JWJEAJf1omzHLZM6IR1IGOAg8gySBXqJhCEGHZ0EQIEYggQxXDqlRIoTRtuKw0yxfACAUKhNFCpAn0ILKnrKEBqw+6V0rKc0xgCKYbrZaXQbraLEkilMIRkYWOHSkFQHymBDHH9hINHQlT1Di6sX8TI5zl5YIKt5S4dmZBmGq5QhDLj7LUWZSGY3Xsb667Offe+m+033kSrFnjg3vcQJBq1UoXa9B4cu8zeo49S37cft3GB7cUWrtZl96EnkKlOwdIp1W28JMUPKhjx61y+UOIHr2zh0OHnfu1LrGiHSbWA0E/5wu98Bn9whS23TmdzjaWzX+OZL/whb//oNdavvk6v7TMxMcHJvVXOLHYJIhO7d4byripmeYJMRniWwXrPZWa0zviYzZWLZ7m+1OX2u59m1LzEWy/8BY9+/H/nvXtdrmh7edf8Lr77/Arvf+rTzN76Cfbvc7jLmmSxdjehOVyCGwuIwwFBHKJnGW7kQebx1oUmoq/YO5GjGbRwpCIWIY6lITKLbrdL4geUqwVkkGIXIvxQsbnRxJUTFMr3sufQUbpbP2Cu4iOCdbYzl2vNJqknqY6O4MchtlsklQ5SF7hhxPy+XbS3PfxBBFmGC8RBSKJBGkHLi8k5BoO+j25oSMvADwIECmUPzxUiJWeZtFodZOBjWgbXlnewdcXmZoPIj5gan+SH5yN8OYLw26SpB4UJglgidYs40UBCFBpgaENjF2KYBsjUv6QFhEBIgdA0lBA36dKwk5gQYriPm2xIqWG0aciRhkAWBkqooSP5cZrh5sg4efvRkdMtr4MZFSknA2I7IUhj7ju+m9Zal1iLGbgDcqnP4TuqvPjKBpNTGbvrRZ54769TnzqBv+3T7njMnDjGxz/8S9x669PMT9n0BxFP33Yfz7x5hV4npKgnLKwukEqDgd7j9X/+Gs3uJe5777/BsAUpOuVSjtGROW47/hQvv/QX/Oa/e4ZarkQsQCUKSYbIFEVH5+rlr1J21lhe8fjoU0/TMOuU7Dy6NEj9bZ7+2GOMjS2wcuklvPQKtbE2M0cOs764xYM/+x50Ieh2PDaDgLXVHk7B4vZH5piw+uyocWxp4khBQeiAhhfYHD8+zWhecPDADK+980Vaa20effeHsJob/Oc/+CLvfrDBzpLOuauvsse6xMsvvoTc1+Dk+A5Lxu1oMagsJSbBDyO87iZe4BJGLlsrW/RiyeyUg4hTMiNCGuCYBttbA9ZbLSzL4Jb9dYTts7XZY2z6EUZm7ycvFdrgLAWxgmUmdJMB/iBEkzbmICKyFNEgIEkSdEfy1vkF8oaBlqYsrGwRKo1MKkzdxHYM8pbDzNQEzU6LgqkzNVFmvd/BCjXCJKTV2MQqWBSlxmS9TBQrgsDHC9vkDId+v0PY65IFAWjgeoJIDoM0qR+QJTGhspjZt59W2yPNtCH9QQzpi9SGRp5miB8brZbdpDg3n/gMQSK0m5atblIhJUCqmwAYHhPDuXE3bX9YkSF1HZVlQ2BpCoRCBpl3Wtd1RsftYYJoALZTYnG1Tyw0CqbNqC1518P3opu7eOLxw1xbXaRe3c2z3/gCR3f7fO31dbaubXPt3HUeeuhDjOZzfPO519ncbuPuLHHLnnlC5ZFEMY7fQiif44fmWGy6+Ottfuqpj4BRRGQphqaG7UMyxZvXzvG1r/4Jjz/1c9hWjlRqKCJiJQjSLSrT7+HP/+yv2JeDB285iCqUiIwSXhQxPTaFm41iOof58l9+heZ6gpYfo6q6vLEQYY/sYbD4Eq43YFd5jP33Ps4n6mv88dc3OP7AJ3noyDxrHZ9+FOCYBqlloqRk5Z3zfO3L3ya39xFGpp7g8MM/w+f/6y/yf/zsh4mNi7xxdZ6zVzaYPbqby+fbtHoCeei3SaoPkwUhuiGQUcbK9U1WFhYx7RBTKerlUSy7iKFH5AsaSeIRSbCkQTUWNDtrjJbKJEHKvvlpAnEQc/wkC4s3MBuvEmUr9MIWK32fTjek03SRlk0SB/SyjI6b0mx5rHd8Vpf6TI7UKE/k6Lf7hIEg9j3GSiVqlSJtzyOngx9mOEXw0oB+GDEyMkbiuQQqIV8pkbh9wjShsdVBFwrX7dLsQxA6RG4PJTxca4IkFETKwAs00ixEZCkpFrqZo9HsQpahZRpKN26CQg6NWmVomoa6SY2kYQ8N/iYQEBI0OQSJGn5AhoZCCYkQcrhPGsP7pfxENwglh99NgUoVQjcgE0jbMU8nsWIQ+vR7JplmQRYNh55oQxeGEFy6coWpXbOcfPjXOGR/jzOvr/Pz/+6PWViVHJ05xPLKdQx9QLT9XeqnPssPv/d37NqzF7+5jaxVqBsxU6MFgmTA0eMnWLp6HdeLkBUDr9vkzPNf4IFHPkGmKQxNYlg6gwyCaJNTd30IXWQUHYdEmajM53g9YPvGN/iZj/wcr174Ee963y0sBicRSZPIz/CyFE0MeOZLv4A1KFKpWly93mX1is+9jx1gsPoyP/3Bx3j1TI8Td2YsvbPIte0mWdGCKGHB26GXSAZuymLbp9+LKdkajXf+mCcf30+cuwX3+t/y7N/8IUZcYL6ww8Uw4tvPLnHfnhIvvLaMyGL+t19/Pwv+GNNTEzS6HdrNBhvbq3Q6m4S9DqIokabF+naH0VGLgmUTJy5pGlEzS2iapNlpY5frKKlRHSnTVAcYy4/Q214mDS/zowvXWV7q4HV0thbbuO2h8F1aX2F5MWRnq0fUz5DKxAszDuybZmqkQEREf0fhDVxK5QKVQoHlzTUSN6Fat+l3B0zP5NhutDACyAKPNE1xXQ8ZJQz6PQzTJvYibLtAr9Mlyc8SZQZx1CBTRTJzhDQZtpPMkhC04TJkITSyNPmJOFbmUEdohkRlCjSBkDoqS39Ch8i4CQSGT/shAoZCWcqbukKghA7aj41/2NUeQ0coQNeGXsXQUCpDSyRoani/NEPUayg0HaGlFJ0y7U5Elgl0NXRUsVDkTZ2H7zzG0VvuYfKOJ5jMr7G88P+wtWwwffTX+au/+h3i1Ws0mj4feOpWXrpqsH5xkf/w377A75/+VT72yU9yfnWJu+76IGatwle++LtY8TpJ2OPs+UXyRUgiRXlylN/6nW8gNEkcJvikOJlOmMTIVJHpGoPIY3Vzh42NNWbKZSIyHEfw97/1Ge76t79LWbcZhAl9L6ZaqFOJWhQnHf76j34VuhAzytGTt/H2D15h0+2zf2KSo7eM0vGu8r6Dx7g68TTzpR+wuJHwnbO7MUsmcaKo5SxiJcnpDVxh8XOPHuKNs68wsesY77vF58xX/xv/36sZd514N89/+Rs8dNcU3XGbN//xOid+4fcQpkNv4waz9RzYCa2dFtcurXLo9jlKhVF2Wu1hpMbW6W0skQQ+vVjDcBJMzSIROm+cW+LUod1sqRN0hWIivcjVi4ukg4BUCsLQx9Q1fCU4tHuE6oRk+UpKmAQgLSqjZUJN0G9uUcrn2F5tYKmMzEhIIwNpS1QYohkmBSdHq9PEtCUDN8Y0JGmc4AUhQkKkTVGy+wT9AWgSYY9hZn362jiQoWcuqZlHRcMOgWgaJMn/xPOHHB9AaQJNaWRZBqQI00BlGaibNOrHOoJs6BV+zKA0jUwpSFNAIvRhYaoAVHoTSJqG0BQqSW/qCAlJiNRNMsHwdwCEgS515FhFnA6R5AyJZmooF+JsKEyy4cBbjh+Y5Wd+8w94Z6nBgfmjRNRQhccp73qCb/7N57n05jV6kUsSpry92ue9j/403z97jsfe/UFef/mr7Cxf5q3Lz7N++QL3PvRxXvnK7/ORX/5zvvmtP8DKGfipIPLhD37/eZJMJ8uGPFBHI85iTCFINIGfRKRxiqZrmFKnaBXRDY2q/w4HD0me+bM/o3JgH2lPECeKVquJXcrxD//jN+ltQKaDlR9w/PbHWW+8wfxsnrql88Mza4wXc9Qo8LY7y1Z8lJ1wgswQGFGC1+ujKZ3GoM3+SoZSMWeuG0zOzqOEwwuvvsL773yQXn2UfTMOzz57kbwc8IF9NTgywUhyhR23jpk30KTOVrNL4IfkiyarnYTLFxdQpGSmxcrldRZ2BvhCp1KzyOk6oRZgiIidTY96rcpONsng8tuM1zRM02Jza53RkRwqE+QsSXVsErfrksg8fqOFUSmRpSmEGWvrG8R+RG97E0TM+GSFarHI0uomie+RJhCEHv0wQRo6fjcgzFIsNTRmpRRKCYQ5QpL5iCwm02rEmk2U3jQ4TZBhQKqABA3A0EAJNHEzmqPJYTYgU6BJ1E2+L6QOSiANE5VmQ48gjeHT/qaaEJo+DLlqGmgZmqkDcqi5MzXUID8B0c3rogSkiVAghE6GwjD0YeRKqaGXyhKkdPTTMkjI0BDasDQ2im+KEEBTGYFIiVY97nvgdqRTQSjQDR0z0/nTv/g9/NYO+XGbzIsoj1a4966PkYQbJHGHi2+/yK6RaVpb61Rmypw6cSde6QDTB2YwqxN88tP/ke8982ck3QynrDN24DZszSASw4lFKRAKDd+L6EcxLX+AF0TYGggS0ijmxkt/gpu/jblTJ6iHX0Ur3s/24jr9yCNnhJw8fpyVxhmeeLQIVoFnvnYGqSKe+ui7+P7zV1AyJEw9KseO87F//Tjn31zjxuXrWElCsV6h1+0S9TvUHLhlfpLNrZBasYqtBQj6uDv/zLvu2s83zlm8/fzX2dhKMXZN8XYjpNU/RifYT6QSjNRj0G2jSYuL17YIspCNbQ9bSRaWVtnZ6RELyVixzOGDNQaDAW4SoWcm2xsJhllgK9vDVnebnz51O2vhCitXVtAtA6dg4fkxOcvh8PwU45OjXLy0wm3H9hGFCcsbDUxNkoYDVBSiOyZZmBAnDqtr61hokCuS1zOiOMLSBW4kEVmMFBqhXiKUIxiGIg4jMmMMgUWmgW6VSW8arlAZcFMQ36Q6SmgQpmBIVDI0ViEYhke1/8lwfxxFullUKnTjX3IO/xIP+sm2kDqahCzL0JBIQ0cJMdTRPylU1TA0nezHkShtSLk0OZx5om56ICF10DRkvcbpkVRx674KrX5EP0wRmSC7SZl0XaGFMeWcz33v+0UyMVT1Sqbo0Q5f/ps/5+DsOK1ek8c+/Ai7947SXbqMm6a88sJ3uPO+J9laXkFPBtQOnuCFv/0yMxWL17/1lzz49K+iTI2jtzzCSy99ldW183zgsU/jawqRCrKbtFFTGu2BR5JoqDBEaNlw5G9ZInqX+NoXn0Go19GLxymqK6w1IUgdel5AqZoRJjpTU8fx0gM89/2zjI2DaUsuvbNGoTZOEuXYfUjibp1n5+ozDIyTrCyvIIXg2tXLJFrIIAxwTI3lc+eY0N8kyM1RGbEIYo35Q9P8yq9+ifVLr7IdRvzyh0/wgxsB+44+zOZml06nTa+1w8Sohhd6kIZsrHTYXN8kiMEyBW6zR7fbo1gsouKYN96+TBpYzFXydNKEhSt9TBliF3dx/8l5Bo1LbG7coDdIKRkWutKwinm2tnps9prsqs3RCz0uXLxOmkES+7i+N5wFl8SYugQtwawdYuA2kbkRoiyHriI0EhJjjEyZ6FIQKZ1ElsnQSVITPeeA1EnJQNgkaTr0DEIbggEQQiF0MaQ93Iz2aMPEGEKgWSYi+7G93zT4H4Mozf4FIEL+i2BWN7m+uDlEN1MIJCoTkKlhOQzZcBoW4iZgJJlK0HTjZnhWH0aVlETT9ZsYlMO/nWXIsZHC6bVBRDNMCa0YLTaQ1ZjYk+gClJKEaYTy/n+q3jRIsuu67/zdt+e+VVXW3tVb9d7oRhNAYwdJgSRIAqRIiJQpUSulkB0jSlSMtcvukcUZzzhsyZbkkRWmJTlkkiItSqIobiJIECAWAg2g0Y3uRm/VtVdmVuWe+fZ373zIAuWJ+lBRmVlRL6LOOff8l3Nuk0c+8BNYpolmmIQqJOht0N76a47urXLx6pDl19dwG3Uuv3kVU7SQwy53vONneOa732TPkdMoZ4Frrz3D4twMLz/zNEr0uLx8ldmx41j5PJ1rz3NjYHD40GkwRgNLKd3EtAySKMb3Pdo9l852Hy9MCLyAi0/9GfOTTd5211muN2dJ1SK8xtP004tknJico9PvdGiGPaSfUJ5ZZDK7wt1vW8R365TyO9xa7qELnZ/8yCMs1ft88ZvblMpZ4tDFsm2O7N9Lq7bFxvaAQ3vg8cd+hNc3tvCTiO5OA7vzPda7CR/50R/h7JE+f/z/XubQvr2olIVta0SeTxJHXFrtc2Nlm42OSxC5BMOAyB3Q90OGYYBlCFJpnVAKtrdb7D31bt7ol8iPHeLmyssQJOQLkpe+9zxhorFda+EFMVKHmhfSr7VI2xY7A5/eYEh9q0UoXPwgQENhiQSlYqT06cUFfHOcQc9HRQmhkUcKRYRJpJcJlUGCTiRSKDO9G0waaIpEOOi6AXG0W7fNUX+vjwQ0hBiRMTFopoGmaQjNQPkeGGpUpaVAkQAKXdf+qecHhLabWLv7W98Cx2KXNn0rwXTTRKp4t5NSoBmjJXgj9Q1MUF64yyCpXbpV7oLz+Ad/U2gCSEDqaP5wQCUvcIOQnJUhiCPKiYYD2ELgGIK7Dy3wy5/+7xgpAwcXkpg8Flef/xLve98n6VqCH37sSTJxwEYvAgeUmaNDmS/96f/Be977OGa4hZaKeOiDv8I3v/4N+jnJ9spTtNduMlad54c//Av0MHjy8SexhcAQGoZhECLREQyjiCRRRIkkERJNV5SzNnvu/ij1esTV64fQlEm/3OGHf+z/oZAt0Fhv8dqFNYZDxasvrXHjZo9r13a4dhE++5nX8XuKufID/NGn34HTHxInAVEw4DOfPoQ/GEISsF1bw9abHDo4yZG5CgVtiX98+i8pagmq00QmPsvtA9w51eeb37nM91b2c+J4iYnxHDYalm1Qmchzq+ViKkHoSQ5MT+H2XcrZDHbGRot9co5FxjQZDAOGrQ5CM/noj7+f4/nrTMpXmc6VMdFG9hU/oNlpkhiC4cBDE4K0aRMbJtgGacdEJaATkBU6jpSYMoE4QipBbEyj0uVR5dV1RMoCFYM0SIRJIsWoyio1CvAfAM8RZ4+M0liE4AAAIABJREFUiVWMFMZuJY9/8J5mmv+//l2oUTVGxGAZjJCs9k9gVimSOB49x+7vKSkxTPMHrZcwdgfXlBol11ufUwm6vmvl0P4JbI/e1CABzXFGuOd/AeeaPsIRaBraW0mWCDAkulM0z0WJIpMVdFohlq2RLcWkKYDqozSfg8cf5G8//2+Rfpn9d55FoIiNmPi1v2DvY59mYf9H+My//WW0QsLYZJp+AIQS3+1SKFssLW/gb/e5/10foN56BalB2tihfsPlU7/7n4lFgViXfPXrn+P1177Ig4/8JLZpoSnw/ZCO7xP4EVKA1/dHBcbrI4YumwOPR8duMn8yyys3DX7o2EH+5DtvMpkBv6tTKKaxzSG+H1Mqldjc6XNw7zSabBP7Bs+9usphVeKpy5t42yH99B4++zchftwiiIZkLIPxiSwaAVYm4ebVNR5422nakUUkYywVEPgJJyZcBrqJL8doXr9Cev9J2t0BuWwaqWvUN1rohkPGlKysbWFZaaQSbO90sXMZcimDQj6LWSqiKUU2Y9PYuERrEPDs92/j9oeknRT1TofYjygVUigp8JXHHYeP0trusndxhmazh64iUo6OEBEZ20KpBDfxCI1pAlEkljaoCE3TUSpB2TYieatt2cUAifwBrYlmjN7T33odNCHekoIhVghDQ8ndQNclGiZilwXSTZ0kHs1hCk1Dk2p0UogRgEaA0EbP+VbQSpmALkY6RLLrUbJNVBT/AEaM2iaJJgwQo2RBM1AyHiWyGolvEgVSoekCIRQyEaP8EfpuLu0+SyLRLY1zhiGxNYXrgkxsbCcmThw8TzKeqjI957N4/5O878mfQjMUQk8hpEn56IcRPuRNyf946jMYODR3hmSUwI5jrJzJRNbi9P2f4Orl87z+3Ndo1a5RmT7N9du3MHImX3/6Czz22E9jKFhqrvMrv/RHCM1EVwnC0vDdEDSNYa9LY2uH9Y11XHdINa8ThEPi7h+xsVHh1VoFr+tDpsTG7VvkCxFv3Nyh1+xSrNoEUpG3Cuj9Opsbb/JA1GW5GTJRTsgUx+g0WkzKLmc+VODC5VExq5RTVMtpImO0SzZWknS6SDsY3Vm93mxiKAMtY9PbapGxb/PIAcl8ZRY99SJ9sUirrbMuq4TdNlknTTGbYmFhjn5/gAScjI2mJAcWF3C9iFwqxXanixu6LK24hGgM623yhTxR2CfEAC9gGAcU8iks06ErBX6/x0ajRcZUmKk0Hd/FktB1B+i6RWDvJ050RMpAaTqoGIGFiiW6thuAGghhjaq5o+8KWZLd3nmEAxK56wMa0aWjJnwEVMVbrYvc/UFIEAkyVuiOOaI+9VHS6KaDSkbM1aiii/8FJ0jQRiKdEgnarh1DGKPL2d/CEkoqQBudZkL+Ex6JE3THHj2vsZvEagSkUfruZ3fZKEaJM4IrGrqdss8JQ6I0hQIKeYO+H9PpehiaQyaTcH1pld/4pd/kG9/6OgeP3IlUEUJITEMhdcm//+07eOfb9zNVXWB1rY5VyPOJX/wDvE6dI3d9jG+9+Pf88Nn7uHnrKv1Y8q9+9y94/rl/QAU9qiZ87Yt/wgPvfpK3n30CIUxMa+RsjMOQYRgThorYc/HDISYazVaD6eoYbr/JoX2SnnGMVH6CdFonjgT5jEInwItNFg4UmcibFMs5SgXBWLaHCm4xdnSKcsXnzvtOsrF5mf1zNs1sxHrnDJquUSxnmK5WEc7oH6yrhLRpYVgOpqmhdEk+nSHUQlSUoBfK7Dl2D7c2zuPWFdfDA+ihSYxkMhVQq/fI2IooliQkhIkk5YNvGcSxZGW9jWOnWN6oYwgHR9gjo6UjcPtD4jDBzuaxLEG2kEPpJoGv02iNVlMmCWSETgDEcULEFANrDKnAU1UwFCqO0XUNGQUIoaFUgtAFUmkjw6faTQAh0IS2CzTFrhIsdwFxwngpRxAlOBYUs1m8JBkFn0zQDB3DMUnUrhN1lwq1TWsEvpEjMB3HaMYuIP4BNBAIbRSw6XSGyA9GwSoEmhy1QAo1smVIObJv76rTJCDEW8EvUTLB0HVkFO2eQCNsIsWuvoHapW8Vo8vlFEJT6GYqPjc7myVTmiJj+gz9AN2AM0fuoN7aRFoeixP7ePrr32ZvOs/BY28j1nXSuo0ydXTD4PS9P8+xkx/jxdcv0+ksYeUPoG9e5/4P/xp//H9+iqmKyfPPPYNwMuSLCYGn86Gf/GW++ZUvoAzFoQfu4+77P4KhgWPbQIJlGMRKsry+jgwTYhEQDHx6XY/pqRK5XArfbdAfToDlEPR7pIyEyUqOXq+NaUPKERRTNitbHaRMs77e4tpGxNEzZ8EZJ2X7fP2F68zM2Tz6wBiv1bJ4RgVUilDqvHJ5jVsbQ5RRYOV2i62NkFIxyzCR1PtVam4eN0gRhRHdxGW5Lbm9nUMzL5Ep5bk9nCKMdNp+gLRS2E4BYdg0OhHdgUvT9YnsFOmUQRIp+p5HGIMtBVJPMCyTxmaTci6NLgziKCSJYnQlCN2QIPbQAFszsHMm7Z0uQX4PobCJlQlRCJo9ImrkCGRKqRCWCRoUSln8MCaV1oi8kOpMBSkFsUxQUQIqAsPAsDU0U8dKpZGhy6H5ChMlB0MTTE2PoQtFOZuhMl6i3RsixW6GGhqmFEglsSwTTXdIVIRQBmgSkSiUUBimOQpcfeRaJVHEScSRqXl2+r2RS1XouzhHYJgmum6SSInY/UKTu99hhOhH7RlCoFsaSmnIKNlttdTo9DMAU0fXBEpXFHJ5xETZUnop5Pix93H5+X9Az6QoFiJaWya65uHMVvmDP3yRFAIvGqKUwDR1dMQPuN5BlKCj+OVPvZuUFvJbn/4mv/PeUyy+792svPoyLkOKOZvWtiRXcpDCZ6I6S5YN3HZAU9P53X/3JezcHBFgIHClJOoPafaG1IZdirpCJZJao08hIwn9gGHcotvcQdcsVte2uHxlhcFggJmzeeDEPCYu33mtTi6VoTvsYwvFXUfzbF1+GigxPTlDf2OLktmkP3eEl66F5OwZRDoh7PeJhEUURVRny2wttdHyGvv3LTAYdrEdMFWKW0trVIo5dppdglARRhqf+ZU8v/c/QnrDBB8wrSzSCjESA3fgkUvlEZZBZzDESnwmJ6bYaLfIZy16Ox0ymQxGFNINg1Hd1CSel1DIpQiGEcpMSCUJg9gDZZHPagycKj4TKL+DYTgkkY8iGrEtCgxhEONjp2xsBUEQMTdbRk8UppWmlFVcvL6Ona4w6LUIbBPlRhgmzFdKJJ7HxFQZ28mztHKTY/MVquUqQRCx6QaooSSWfV68vMrCzCx9z8VwHDQpGLou9y3Mcr7WZ6ezg4h1hJbstmkaqBGuUMYIDI8Y/wRNUyipIRTIWCAsCxUHkEg0c0SfSrnb/uxWfs3QEUojiQJ+cOzpBpouMTWHwHchisCw0XRQpo6KAnK5PIPBAD2Xs84lQ8F8OqDeq4DdoD+QKEsy6CkOnXo399x1H3ESg2EhURia2F1pObqAzzR1dEPj0ff8FO97/yf41pf+iKS5yoMf+jBPffdb9H2YzI6xNWgTax5RpOE3G9hzczSGXQIbXrr6Dzx830+jEkWYRCRRQC+JSOIIkYS03YjQi7EyBlEYIQyB7wZkM/nRhRmax43b21QyaZJAcmAuRSZRRAgKZg6hdOLIY2pumun5Mwyw6CfjOEYXx6mQKpwm0Uw6oYJYMj5/hGTQZWZyjJtbNeJIkSs4TC9UuP3mFs1Gn2Y7xATKM2MEOyHZdJaUbqNvt7jSdpC6Rs5ySKczxIlG3HdHG/Zsna47pJBJYTspBv0uecsYia8o4tgnVyzT7TZJkgihaYjIJ5NJMRx0yadM+sM+6UyZsDyLYzq4eh4tiZBSjjxDu5hAtw1UFFHI20wUc8yMFzk2liWXtji1r4zvDSg6HouTVYSWUMyaOJaD5naZnx7j2OIcC+U0h2bSFMoRRdMgly8yWVL0vIS0abO8uow/aJEpjXN0OsUrS5vcd6gKUiGSAVN2iqzlMJN2uLnTRrc0ZBRjpGxkkmCZJok2MtmhSZACTehIKTg+s0C9397FBuGIjdLe2h2WYJk2SZKMtAmhofSRPoGM/kn5NgTK9VG6hkgk2BagcPJpoqGHYTnESYSMEvR8ST83cEO2vQ533j2NHlj045C5WY3+9gS/9Qs/R6Y8hxSCMI6RMsG2DbwwRENgagIlFEIpZBSApvjin/8H6s01uqJCo7lORrnEgYvmR1gGDEWOXhTR83b46Cd/h5//+J9w/4M/homgN/SJYmi3h2w0PRpbLtvNAe1uTBSHEHfZ2mxhp3TSyqDvDQmiHu7ApVd3iWWMZiSUKzn6YUKiIoa9iFY/IYx8qvY6tcYbXLjwJh9975Ct7RZBa8iD9x7nW5dqmJpBebaIsCTZlMb61g6WnUIlEfm8Q5BE5KRNIBNCLyCVsmnsDEhkjJmyMFOCe48GrLQc6r0hpmbT6TUh8JFRTGyOCJa+75IxNIIoot9tYSDo9tvEgYuMIxLXxU9CbF1HhUOK2TRDV5BK6QRS58FHH6TvgZdopDJ5CqZDN+lgWzpxIMEc2REMTWNqcoyUniKXkqSU5NCMgZ3K8f1LVzh9rMCe6Sma7RZTpSlu17YZz5gcOThHt7lDOmVhp3XGKw61jTU2Gi7HFme4ebvPeE6wtLrBm8sNHrv7IEXbRrPgyKEcsShzcjKDaVsEcUS3t82+coZarLB0GztlkU/Z2JaFJgTC1lEiRCkNzRj1+HbKotZpovRdm7fQRxB8124hDIMkCUcYQttlyZIEM2WhpMKwTGQQj943NITSdvH+SHeIo5H9NZVLo6LRHirdsdQ5qWnMzabobkQ4mRam1Clq8FM/9du8cOE6p+45S8/zMCI18o2oBKFGlJi+qxkkQmHvmvI+/1f/F4XpPfzq7/wpr3/1D6ikbWqeT7Gq4SuFoXvY0sDtJ/z6J3+fMBE4wqAfuQy9iKEK8XshW70+MojwlYdqDSGvk0oCwiSm1fBo9n3ioIc3bBNrPo2uy7DV5NDJeVQcE6QyjJdsrl5uUOs2yGYDCprkrrvb3HnwCJ///C1+8dd/guG2y6Qd8nJ9QGilqG15bKwvEyYC2xY0Oh6z4xVur+3QbngEMXhRxHgpT893iYKYamW0usYLBGNiHTs/YLOdwdF0crkMrh9hOjaGkKQsE2/QJYpC4mEPWyiEIYm9EOW7OLbBkTuOUdtuoNlpyuMVvNDjyNG9PHLvEc7ceYrY3WY86/POU1VmSxaVUg7V8Wm3YwxHIwkTDMci9jwGnQ7jZZOUbTJdzuGHMXHS4ENvP07atnjplRvomUlEMuDdpxf562deZyaTwxnP4g0HpJUg9AaYRpkkcXGHQw5NpbmwvE4uZXDm0Bx9IRhP68gkwvFd7pxP870rdSYKNuMTGQ5USzS3bvPwRJ5hPs2pYo7y9ASCiFLaxHUD0rqN6weQMdB0wcnxaQ7vmebsWJ5I09gZ+iOgro1YLMM0kUm8e4Lugn59RPWKBORbImGSQJyg4gQjNTodTMMk2Z21CHd3OyEEeqHqnAsGirQjaXmST/3qV/j2176EMiWPf+SXOP62B2h2h9i6jTQVKdNAMPKBaErik2DrBlKCO/RIUKysL7Oxdot3v/fHcBt1vDims92ilDMZ9ODBsydomS3cSOfGra+w98g7iaKItG6i66BkzGbHQ5eK3tAnCH1iLWYsFSDxGUi4eXOZfn+DUjEh6G/jJ1DOOuTGcgQDFz0NoR/SWunS9SWZagXpQWWmzMH9s/zVZ19mvALb332NN+MMV68oBvlx+jseugopOClaOx1iHDSlGPT7FJw0pq5hKMHAc8lazsgCrVsk/pDy1BRBbZthq45mVGlHWfrDDm4UY8gA4gDDEsTDHjIMCYej2RDLNImGPulihlTaRtM1nnj0FJdvbfLJn7qPg9MzHDpwmJl9Ezx/uUUgFUEcMwh0pvMO5Ywgn3YQhsaeuSzEEc3BLvtlGszPVsk6AtXf4dZ2jQdPzLKzPWDfwjSDQZNqIUtjs4Zj27x89RoH5xexTZ+5sTE0XHKWoDo+Sd5xuXh1nQPzY1xe6nHm9CH+8emrGIbG8tpNSpbGQFM8Olcm128zLAjqOzuksmMIzeS39iYcW8jxl+dbFKoF/PWbxMUxVlY2SJRAEZNJ2bi9ITPVKtJ0eW96yMGc4uziAt94cwP0kZpvGCaxjFGxQtMMEBLtLZdsIiGJ0S1jxKopOSISxEh7kLHcbbOAKB69tut/0hfm7HOer/CFxJQBk5kS9z7+JG++9gIXv/993vnwgwgjg2UppBLohkYUxyOgokAqRZKMgI3SBQZwfmWJn/6ZXyOTK/L5L/xX/NoaTd/DzTskboAOTKYs/sWnv8YgKpJ2ZigWsoShSyBjtFhRW29R325iqpBOe4369SVm8qM5h521DVY3t5kupGi2XZ65sMLt6zusrrcYDhRxZpzl1TpeV+GHkmGniycUlXSWzVbMq6949JIxHn5Y5+R9U6xdXccubnJ5M43fDzBsHYVgOPCplLO77IbC0AwM08SNhhSKRbrNbTTTBk1i6ga5bA4/7jJfNpjKpNjoxiih86EnHuLqxSt8/Oc/wHvuOc0LL7xMkoQIY8i0Y5E4DodPHeLtxwtY0wc5dHwP5UKBfXNjoDKsrGwhNIPvXFxmojROGFp43oDYcLhzdoKL6wNudnX2judJcDl9oMDJY4vsnarQbnUYy2ew5YBOZ8jDD89T22wT+BJDH7Bcc1GazXwhxQs36hybS2GaefBDNOFihwGD2KfX8skXc+SyOdIi4alLl3j45B4SM809p49QzSZU8nlmsym+f30bY0wnn59gu9Ym40li6fL2YzHje+AbL/WRqZCPHVvkz797gcMH9jA9UaHV7mAbBr6MkUKSJIorbY8f2eOwMdS41IsZeh5JrEgiheNYJHGMKcQowKVA0w0EGsIaJYKMEzRzpKhrCqSpYSSMtmwkMSiJk02jdBMlI3Q/iM4Zpk6upChNmPz4J/6AuZnDfO2L/537Tj9ML0gY27+IaYzQv0okSRyjx4ClY+oGnorRd73nUgjuvuNeCnaGUPl84TO/T2xKUprAEhIpDJzYotntM1ndx/E77mV1a5t8XseNI4QfU2ttc31jjTB0MRxBo7VKc2ObF1fqrC2vcenqNaw4Yr01YKu2QxiFaLqBZRk4us6g2SCOR+yGTEbDJ7Gp02n36fkh5WIWpWtcWclx8UKHwxMlnq2NoYKYGIml65gqRGmCXq+LmUi8KCSKfaIkopiyGaiYSrGMTkwUJZTKJVbX1/AiDzMOudIVnL73DMGgyX/8mRO874ce4WpjjdDvY6UK/N7/9h4+fs8eykdOsHhkP7/x2AyXWnB6Mc1cNk2r7XLs4AzXVmt03JDQKKILjVAXBL6HaZs8vjfHn720DVaKqazBr378YV680iIUWTLGkFZ/i0NzBnsqZRrtDmcOFFnd7FO106xsbqJnx5koOKRNgy88c4mzd57EZ4J9pS5BAnv2Zvj2iy9z9MBelrZdkqBDU2YplwTHDx3k6W9fItFsNttNdjab2NUxLGHQGXo8vmiz7sUUMgU0PeHsXJq1uMqM1+TUlEtNn+LVlU1OnZxisNIGfUA5V+Du48dYabYIE0V3GOHYWb7dGPL8xjZSN3CHPqZpkiQBsUxIZTIEYYhuj7oUFSejrbSJQiiBmU2Tyxoj/cOAxB8RQZZpgWmgawaJptCTGMN00AsFzqXLaVxf455Hf4nB7VfpBC7XXvweH/u1f8/s4hFSuokvYwTgeQGmaRCTYGIgE0mQRFiahmkao9tmlCRl6mBEvPLdv6LX7tJRCQUgkjGdYYhEcvni8zRufpfnv/059h9/hG5vQLPdZXm7hu75rNXqrF27Rn11nW4YMl8s4g5cKtks3XadYdcnCELSmkbkDRGaIkoUKQ0iqbBMk5ShkdVT9LsDdCLuqLxJvFOjX1/l7QspJrWAxnBAJzIZhjYpHRLfBxSGUGTS6VERiCRpwwDD5MMfeITg2i16gz5NLySTcpChx3hlHC1yec/j9zGz9wCTWTj35J28cPEaj91/nC8+/Qb5CBpunyePZZif289UtcCrtzc5s3eBtb7H6ysWawOTN9c6XFlqsmcmRRw7OCmwddASiyeOpJnU+tz0dYx0kYqtoUzBty4t8eB0zG9+/CT9tXWcKOTs0T2QVpRtg5VmyJ6JDIVCiks3G+zZN8mV23UsDU4c2M+NLY+yDUpIpqdzfP7vXuGBe05y/pVb3HnsENZ4AafbxzGglC+TKqfZN1bAcEyqU9OosM1ff+cK09Usi/tPsHVzic5Sk/1HyjxR8vl2LeaeCZu5Q5P84Qtt9o1P0enuYFlNKjjMHd1Dsd2gI2HgQdY0GIYhUSzJZQvohs4g8JiZmmbguag4oZAa6REKgYojnExqJABKhZlJEfk+uiYIgwClmcxVU/jKJJUzyBk6VtrAHUqSeIRP9Eyac5oZ8dn/+n0WDh7j3/zrn+Xk6R/htWf+nqHnc/TusyNTXSwxMDAcC0MJEpWQJJJQKPx+SCqbwpSKfhATBCFSBjz/0rNs1S8yM13ggx/4V9zzQx/jhe/+DUqBmdbxPI/b6010w6BQnuTajTpb9R6yPWCjvsO1N68QhkOCKCSREYEX0+5s43b7RErDMSRShQgVo5MQhpJiJk00HJBEHkHs4fs+mkr4k9//HZ555jyPnHSoq0kq84f50Md/nv/5d3/FkYNTeHKZ49kdNrt54giGsU/aNPHDGNfzmJ0u8MTd4zz/xirvOjvHkbcd5Xc+/DYmDzn87Nv38qv/7J18+PQMi5NVzEKaSAhOVgI0v8a+8Rn+41dfplqZZp4NPvmB+7m01OX82iqnqhrHCzaD0OfFqx5Teo65tMYT986hJz5bax2mJ7Ps9GKubPeZ0U0OVAsUbcl98+PoURPDSLHVavPoHoOz82Wk7DHpZJhyDK5t1Rn6JfSJKpVsQsXO4Ss4PDfORqdHa6eHlp3k8FjEiSMTuMMYX2nUezqnT98JusnLy8s8fvcM2WgLa/wgqYxOIhJMdAIk3zx/k6X1BvvGprj/2DybYYo3Lr7GO84eZzaT5/OvLbPUWEO1DdaEyV3zgs2lJjttgW2CU5pkemaKX314jP/2zDp+DE5KJ0ZRcWBudpIkifBdl8XpCda2a8xXSpSyOQr5DI3tNsfmx2ns9MgX83h+gGbpI2Mgisjts29hjkreod2LMVM60SCiVMqilGIsX8SPIkxDRxcG5/7vf/N5Pvkv38O7Hv8Fnv7+F/jIR36Wi9/5Kud+67doJebudNNoDE/fnWyybIskgjBJcCwTTVMkSIZuwMDzGXpDCuVZvvX3f4IUEyw+8iGuXfgK7dU3yfsJmpcwNp9jGIMjY26unQezQqtWx+32WVu9SRgP6fWHaKagqOm0WtuYQuEnAYaMiOIAdIFj6AxdF12TnDw0QaO5QzpnUjAsis0b/ONn/zN/+o3XePxdD/DEA+/l/OpFnPg18vp5VlckG7UGf/nHv4dXnufzn/owU2eLLB48yU++a5FKv8WX//AXuLHe4eTdk9zz0P28u9LnK1eazO6pcvVGh/qgx+r6BpP5DHurGVQiWept89hCnrnqIb58scGBqQrvODlGtqDjdYY0Oi0+9MDbyDg+mw2NVgeWWzlm8w7pfJoLt1qQ6MQZxbNX+2wOYrpuxNowopTY9KIs1+o+X77S5e65EsVIY7I4xpu9FJ2W4q9euMG9R+eYLdg8dX2JExMaw0inbecoOpJ/+NarzJbLnDhY5fxawO3NBoN2GzNf4eL126xsdXD7W2SshEdOHeepl9c4uLdKJq343Nevo6MhNZ1Wt8382Dh3Lh7gz778fe46PM7paZMwN0VnZ51nNwLeeWKG7FiRnzvo8WwzTVTvcubYAh19wHghy8sXrpPOZfn4Y2k+94zi9JxNdTqF0gzO7J/mhfMXOL1vErOYRVM6jmNSNHX6Ucz+qsV0VqfVdTl1+gyr9S3CoUcql+XgnimiRIKm6LVdGu0eGiYKNdoZGwRknRRu6NLzYkyloU8WObexepnDZx/nv33m1/nT//QSBUNnvjqFLM+hORn8oQumTihHlgpd1xECvDCg54VoaASuS2cQkLYcGv0BG/U2L730fQbdDc488DFSVoXLL3yVwXYD09To6lAoWBw7+5PcXnodtxcShGV6fpt2vcYwHEAcYmU1PvCe97Fvb5orV67gWA5+EGGIGE2CSmJ+7qMPcePGLT7w5AP80hOP8sidc/z8++7nv/ztU/zmp36RL7++wurOkF6rgd8dsnTtKfJGQCL3EK2t0A1ha+s5fvQd7+Z/rvS5p7pAvVnHcXf4xJN3jzY+OAZrjQztYYNWNLr8fasV8+Bkj4MHT3P34n4u3LiJlY44dHAPD+4tYpqKRk8RK42razuU5A5HZo7iuy5TUzNcu7rM7X6BifwYV5YDvGFIEEccX3DokmW97RMHDr6WZak1xEsUocxiOmkSGVNNF/iRexZIzBzVVJ901mI2a5HPZ3nn8VmMXExpXDDomSy1dWLT4ZkXL3LixByp4hQffPAIDR+Wrt/gvlOLVMt5Nls7jKczuL5PIZXl9qaHJbY5c3APQ1+j40GxPMUDZ6o8e2GTI/vSGNlxlm4uszBf5sKtJm03IVsuMlkusHR7g2J5knFHkBtXXBuMcyYP07Oz+JrDV56t8bZjc2wNY+aFy9mxTa64edY2A/QQ9h7aw9x4no2tDkFvwFzVZro8zU67z7H5FMemx/jCM69xx+IiEpecoTE2Nja68jeWKKHhxhJlmExPjtNxhxiaMfKC9Xr0BiHucAhCYaUd9MX5yrn19VV+8Tc+w/sf+xkCFZEojeL8PjyhgTSIZEQSjbJKCg1bKPp9n+2dDr2WR87S2PZ6hH7MxaUVmq0+fhgAWQLDAAAgAElEQVQS9yQzB+9Dtwq0Oz3MbImV2vcYGnlqnSGuG/JjH/o4hw8d5eKV8xiWy6Abc8fhBQpCY3t7i9/+5x8mbQ3QCTly6gSnTx0glyuzvLzOJz7+fn7i0ZMII8PC3Czz5RRRX3LbG/LFiy2OHD/M91ZabG67aLaNZmaxfY+MqNFVARu3IyrxgFiH+9/xIdqZDM5gh69dqPHBfSa+5/C9nQFVq8yz15vUhhYLOcFGoLFoNXiwOoc36LA4U+LvL6xwz2SG0LXZbDbJ6glfeWGdXC5Fmpjq/grVnMlmpOMPVxjEks++OuS717ZZ6kPa1ilmbeLEJq0nLOYD9k6PkZUe7z4xzWZ9hTsWZvgX903z+ILi0aM2JxdzHNsree3yG7z9eJEz+1JktFWeudimE67xzRcuc8fsHGcOTnI7FCi9xAN3FFlb3mKmkuejDx+k2+hx9t7DXL9Z44XXV9m3bwZLh8FWi8mFIscPV/mLf7hM2w+YnrAZNFpsrW+gS8ncQorILRB0+phWmr3TY6M7Cs0KA7eDFwes93yqVRuMFN6OxUKuS2TpfOelHktRjR+/cx//5WKTo/vH+O0vXOGJBxQvXpykMmnQTRI2VmvMjSn6kSBfzNN3BcNeh6l5E004fOW513ns7e/EHXaYnyjy8nKNvRMlbtTbKKmRyo6sH27oEYaKXDGNNxgSIRBy11XoGBBCFEn0xIzO3XHve9k7vQdpZDGEQSJjtN1tyIKEVntArd0mDmMC3ycgZmW9QX/Yx/NjOn2XqB+z2e6SdlJsL68StJrERoyMQ4LIp9trst2o8+M/+i+59+RdxGGPtz30BCfmy6Qtg7fd/TjbW22G3R5//K8+yq/99EeQYR2tJBn2dhB6DqkUM4U0maLOux66m7TW542tgOkyZEyXkqHz9LpibznPxx67g++/col/9tBx7jswT621zdJqjdqbT5GZuZ+DJz/KV7/2JZa7CVoahoNbvP7is2x5AXfuP0AhhomsThI2OL/S4fUdxbiuuFR3KZh5YjvFM8sNhDHH5S2Lk5ZFXyZ0hUXg9Vn3bQI9IQwSbvk13nv0IKEXc722w7Y2znPXuxway3HmwCK3hy6Dtk/ODhhGAY8/tJfpGYO9YxqnDuWZKMBHH1jg7LzNeC7DxFQaRUIShbhdHz8KeeydD5JKxaQdmzsPzeLgIuMuncjgX//Ni1i5BZoby4znLVL5KVw/Yags/vHyMl0ty5V6yDvuP8PyxjbCh1N3TdH2NBy7wmR1mkq2xM3NPnsXF8lZislqlZcvb3H6jpMsrawzcD1K5TyNRo1lN2KyUsAyE0Qco5s6tze7yFSedH2Fp7dTmOGQTDGNGyacqM7yp1/+HieO7kVXs1TkOs81GK0stSSuMnjuhavccWiKfr+LaWYxNYObaxsUHYfX1rvkhIuw8rzr9FF6bsSeiRTVqXHWl2t0BwGmniFTcOj0hpiGPXJ3h8nuvMVopaUmBLppqnPv//HfI9LThMOAfuASRDFekOC7IWsbLQZBwE6zRa++zWqjSXtnQOAFNLZ2GPQH9L0ht5dXyGQt8o7BpTde5ebyVUQwREYxG8vXKRfSbN1epjhVYrU1ZOHgHZzaP05jp8ntzQ5uFJDOFJneM08Qw+e++RSpgoNy+1QrZcqiiTLGmCmNcXl9m7Dtc2nNZXxshp21Hht9gzebFl4iWCxOUZCShbk9qCBBBB5rnYiydZu1javEwYDYUPy7c7/C1/7ub8mXHAaxz73v/0Xef8dxFtJZXtpqk5Ims9NzdPQCJwopNvptNiOdx+ZjDqYT5tIa59c9Hjoyxmsrt9lxXYp2yHvfcR9+s06Usgj7LZ686yRff/VN7ljcS33nNt2uRMWCglXgol9HtxM+cW+ZW/1V/vkHj7F86wqVsXFazZByxUDoGnEsIY5JOy620NBNjXAwJJMV3NzY5KDV5W++8iqTE3m626vMH9lPQfr82eWE0vg+GqvL3H/nXm42YrzYYquvsVJ3yVUm8AYmacukG2ucPXYEkXYYJgXcbkLF9CjlHN6odTk4keHPv/YCtUHC8cW9FLITXF5dxmttsu/ILBnbYKI8RRJr7Jut8vRrmxycnyeOPIoTMxSyIffOztA0BW3psFVr0vA1tpobZOcP8OqlZeb2T9ExLLodn9nphNdutSllLY7unUHKIUubNYoZwdZ2SHPgUqmMcc++ScbyaV671SKbiqhLj9v1LnOVaSJdp5wr0o19NF0Q9UOkgiQMR/MQb81LaAKla+iGI87NH3yAIIjpDHpEYUxtu0Ntrcb1tRXa7W1qGxs0NzfoNHfotFoM/TbTMxNcvXoZtIR6bYO77j6OISTzk2Os1mssX73AIPBobq4Ruj2OnTzO+HiWQsqkUjAYc1z6vTZWSnJ0bpooapOECjOVYzBssGfuADueC7pFNvZpxQ6HJ1I8d2UHS9d4fb2PIYo882adVmxQ7yS0uiEt3aLtJsQyxws366zXetxohwR+xPVehsrsQ5w9eidfu+Xy+vUB3e0X+ciP/u88evhRIqfCC7eaPHN5k71FhzoZNNPmG9fWGQtdulnJY4enSSeb2FnBtT783PtPcWFjiePHypyZUEyNlUmV83znwg3OTGjsnZrkuRu3WZwd49XbO+wrj3NjbY2ZapZ60GcgS4yLIR+8f54vvTbk3j2T3FppUHAc7EKWYBBRKWX50rMrzIwX2NySTE+lMA2dyniRSkrj5GwOy0o4cXyci+ddSnsP8LnvvImgzDMXL5NNj1Mt2qx3fNJOkcvL21jFGdxQsLLu0hoMUIZJ0ciyutPnRj2gEwquRzlu9tM8teQzlSuhLJt77z5NOdphqRuQz9iYsYfMFrFyY/gDRbft0vSHdL2EyTRs+z7N9RrjBYPYh2tBjz/77ip2xSJlZ7i82gQtQcUmO40tDs2V+dtnljh5eIGUk2Hj1gpHp/YhLFjZGHD4QJViKUs/kkxn07xZ30HGBrdbfQ7sqfD3368x7ug8tK/K352/yqOnzuAOt1nZ6DA5O0W31x/NRESAZY2Wk1kjoE2SoJumc27xyD1UKwUMJVEaTJQFge/T2Nig39mmtrlGr1enPejT84bMLx6nttlgdeUmk/sPISybWmOIlB6vXK8zCBOqMweZnD5AqWhx5MQxkhjMdAZLCyjlDWwdpAjI2A4q5bB0Yw29otOt1RgvlojiGGla9HbamNkiwyDm/JLHE0f38+KVTW7XhjhmDhELSjmHRLOo5IssrW9RKIzxyu010k6aYnGCDAZLAw8DhzAMODBd4ttf/E/Im89iFxUXX/keHX+Tr9wskDJ0Dk2WUNJkpz/k8jDiVMbCN01yZo4jRcXQlziaRc/IUF+9xkNH5jlQKpIp2lj5OVrNLfq9HSr7j1BJT3B4epycBSfmqxyYLFJIJdT8NJc2O+ihy/RYkdpOjwSdK6s15uwsx07P02126DRdGhsuW12NrJVhGOfxw5j15YiBF+K6BuN7prFMRbtr0tbSfOfida54Wf7D8x3uv+skwcDn6lZERxZpeBbdvsYbOyGbg5iVfsSV7S7DyOLFpXW220MapLm4us3OwKXvRoSaYN2TbPd0Xtns8fq2yc2exc2BoBukmZio4mgafhTx4rVVDh86ihX32ewKjkyOYWcy+EHE869fRjlj/Oz77uHPv34ZLfS5+0CJflTCzqQplMdZ3arx4B0LXFqpoWsamzsR1ZkMPQ/2lfO0XEEpW8Z2Mti6yfOXb3Pq4BxjeYOLV2vcv1hlc2eHV2/WOXlwD6Zo0fJ0dnrbVCrjNGtN7FyaJAbHsYkFGIaJMvXRpF2mmD/3wIPvQZN9bAeIfWwiJquKsYzB0X3zXLx0lSRSzB26A7s4xsBL8BIojE8joxCBRqT5uF5ANSdQcczCdBohfe49NUO3tcHxE3sZdBpMlcdwHIN0IU+v7XH59gZz05MMu5J0rBM5GTJpQb5Spr/TZnphD8u3WtilFItjY6zW2rh2jlrHpePFHFq00GKDqUqeRn2Lg1NTKBVRLReRQUDWsdnc2mZmdobnb9ziwfkyl9Y77J/P8ugHfgKsHNNz7+JKNIOhm6w2erzRDXGSDFv9mJVWgpmY+CrN9Pgit25uoYkUsZMm8Pscni7h+j6GCnjhwgqh5nBja4Uze/by6o0OWbOFHxpIwyPWHK42aui6xmpLI4gF+UKZ+k6H2ZlZfmguz/3HDnKh3uK751fxPQvfTTOI88xPVmn1hyjTYKKQZZDARDbL61eWKGpw/s1NXm67XKrHnO8VeH1pyD2LM9SbCc1Qo6GluVRzqQ18btW79No9BgMf1/PQE53u0CMOFZ406Q8Go6HlMCHWFDKMkFGEL0YL4DAMUBALE90q0HQdWlGaXpghVfr/iLrzp0vTu77v7+vel7Mvz74vvXfPTM+0ZtFoJM1oQWCEZLChSKlIYWwXXn6gyhUSJ2VUdlJJcJlKKpiElKk4JOCwGIRACISEpNFoRrP0TE/vy9NP97NvZ7/3PT+cFvwLp+o+9/e+ru/n9VnjnV0XJ5d4dz/knZOIMLXYckzyOOT09DKPOifMTM2wNm1RaBZX723z9PkVHN9By1Qsq4FGQSZkJuanmTYUXCclt2wc36FUmuDBxgb94w7tySan11ZQhcYHG7eQJYXNoxGvXr7AI9dhqVVHk3TW5qY57PqsLc3SGY2YaTcZuB62ZZNTkCUJEhLyp3/yl74chQV5rnDYC+l64wKN3aMemwcpe12fQK1gt+bouSEkEjk5gpSGZZDlEUgu5+Y0wqGDacPF5QlOL6osTBgcHHaYm5nnuNun3SozigqOTjoohcz1+49YX59DiwKc0Kc5XyVNfSRsRBaiWDInxw6FJdHWy4xynSiFxzsnfOaZ06wvVEldD2fQ4aVz80xP6kgk1IyIhbkSz12Yx64KEini+5uPWZ6Z4PbWAEcVfGz9Bb759m9y/7132L3zDtb8OYpUEEQe5+sTdAYeW7EMKOx7cLfrsXfiMYhkGmaDgeNwv3PI4vwcF5sl3MDl/rHDmeVJhKSx3S+48tQSg8MOG/0Rs80aW4d7vPko58MTHSfTcCWNes2m0ATvHYyQ3IS/vnGDemOJ1WaZe8cpMYK+53F/f59WY5qD/T1sQ0bJQg5PfAxF4u3tHXpC491jja1DjYwSZs3mcCDx+p0t+kWJ3c4INVHx0wiBMl7DkQQiG0NghShAU8mLZCxQ/BAIE4xxgeIJ+/jD3LLIqE9N05RUDvwBS5bJ3c6QuWqFdtXgncfH43RbCoMwohvneJUWV3sFmwcRJdNkz6mx7ZQoV2bo7G9Rq1R5/8N9dgdd3EDlwmydP/3ODVZPrXBrY5sgDHB0m++9cZW5xWUkzSKKcv7qvTvUa2VW5hepNMp0Bi5N22a6pPBH37vB2ZlJIhK8BIJhF61UYaJscjD0sMoWgR9SNnTCNEOuLj3/5UM3ouN6dEcRPdcnSTPiVMOJY6JcpeMGxGlOEiZopoopZZSNlKqRYso+L6xVEJlDRZfxopiJepnb944IwwDXCRi63pjKlHUUxaAoUrIi4fB4j4XZBif9Ls2ZBYJhD1OdYLaVs9QO2e5lTCgpC5MtJFlCk1MiIdFaKKErEhuPdzl/ZoZWfQGsnEHfRymZzDTLqFLC41GMIUn0PInpiSoltcDPDGxZY3f/MacWl7l14wNWzs7y8Ref4R+9cIETZ5cvXFylURUcDB1emGkToeONPBaaDY5Ojlid9Fg/o/DC4jRtU6DpJounz1MyFba3DjD1KoMkJAsyvrtf5pt3PH7nWsrd/gRfuRfw3laCECWu3T3h6KRHrdKAUEY2Smy5Vb51q8P7OwEvLE7y9e1dnp+ucNvVCEYBh36E44a0q5NEoqBZt3GtiD/ZrnPrSOLIT7h34HLsJdzdOaLWajMKArIkomSoyLpJmASUKmXiNEOo5nipTbewDYM4Hy/JjZ+AH4b2i7+xAMTfpNDG/Q2tapWd3X0O4oyRN+LQ8dg67qDIBaoMmm0SJzmSkCj8lLPT08zYJSS9xDtH4zrfx70TdtQJNnoyA7tCLzI5psL2dpflM3NsPhwyFDr9UcRg6CNbVVI/4v6gj5/JNC2d21tH4zSnrrB3MmB2dpGDfoda2eL9zV2mmhWKJOHE8em5Hs+vn+HW0QFFVKAYBl4UoqkmYvnTv1hU9DJeEhAGAaWSja4pIBSiKCKTBVKaI8sympxSMguW2g3KlorrHjPRniL0t1mYaGJZGrc3+6wsLfJ465C5qRIxBgMnRQs7lOolhq5MtaniOT7OqIuqVCgpJquLFp2jDmunqrx3p8NTS7PUlR6u4+ENOuzmVSolk1FqEokKw35KS4tRSwrfu7rPx55d49qNTdDLIEsc7B2QaTKRBysz89wbhSiyzsnAY5SEhGFI5eZvkgmNdruNb11kM59lqqbz0x9ZZHPvJlXT4J9+9hnU2OTRcYKbOlzdPuIjz9TRMoXc61EoBv3eEQeOoNVocBAabDkFQ8/n6laKk457E+RCsDjTYnN7B8usUlViHEVhbbKOlsHpqoITBDy3OMWDh494xxcYWcT56Ul2HIfRySHV+hRzSowXZpydnWLCEuzFA64ONO70cg5OOkxPN4mTHFPAstXgzvY2PTmnkgtM22YYxTQ0g12nN6ZX8hzSDN22x53PioIqSaReMDZ2n5DyxZOKKpQnVpIQFFGKamgkjkdrbo7OsAtxiqrorC4ucHfrIcvtBn03YhAOkIROHke8dmqJMIxxVR1FzvhwY5s0+iFx/wQOkGWEOj5hy8N4/PFb8CQIlCHShMLvUxQRbV0iGPnIhsb68gKqqrDbPWRnv8OF5Wla1SaWlmBVpjk+2eNgEFLXTd558BgK7W+EEElXUJYbNpnwSV2Bouh4cYSEQNHGIJSWC/SyjU7OUl3iU09N8eatDlNmRmuixMHwhMvzMnIpolmbI0lDQr/LlfNlNnYCtvcPWZqv0Qsj6tTwHIeFygllexldO4NZ9GhUdf70gw2eXajz9vWY2ZrM1/76Lr/w46sYEvhmk0Y3RpZ1bt14yEsrDexKC7+TkEtV1tYX8fKMuFqnqUMidOJCY28Qk2Yyh4ceA98lVWTkAkZugGVZYK+y1JDoHW0h5idhpBDIgv/1zQ1EJlMUCt84uE3XTVlvTHG3e4ga5/z62z1KtoYkKUhaSlqYuGEKSQ/FqpFFIYWUoSgWWeIgVJ0MwaPuAJQSYZ4T+gW5FHEjPibLVEaeSsVscmvXQzUm+dxci994400unJ/j4Pge97swKxWIZpMiSfjqxg5FkdJoLvG97S0WKw1sQ2HU8xCKTI7Eu94hYRzSqLWIRM5gNESSVXa9LkI1IC2Q9BIZI2JyyGQKUlJZI/8h7wgUshgL3+POoLF+IStQkkmKAgyDbrePVIyX7JI4YPtgF9O0MGWdR84xC+0ZWirYlkbFamIqHvd3tjkZBH9Ly2gKKBpEIahjeCwP4nHAR5bHPlMxdqIKRUWuVsiyDKekkmojUsdFKnT63R5ZlFI1dbonPuSCmw+3ubDYY3JxGcU9IRQKkqwhqRqGYuKGDnkUI8+ce+7LhmIwDFKcIEZOCyxbf8KBaChyjpGGPLOuszBRwvEjLs4kJFGXj66YqDjcOZGpahJJ4TCpS6hVi+7wBDU30LWA1YlZnp/XqE9oHDg+l5Zmcb0AEpeFKY0HnZCfOrfMIIOKiLn5KOGhq/D6A4/re+AVFg2Rkillbu04zNUn+Ma1TTqyzbu39zgeRZzs9TFrFrED1+4eYJVzlFygN+oMOw5JEhKh4Psuc+USWh4zKXbZ3d5FtRS86U+g2wZ9PxyH03UVTc5RJQVN1tkb9dAkhTTOkE0D0yoR5uAFPmWzTMvUSDSFOItAEQgho0kyeSbGdOIP2y5FMcbBhEBKwVQrlKoGs3aTlaqJhEJYyORRhlJt4e8fAjLdJCH1fMIswbKq3OiOqE1Ncf94xFqpQiKPA/pl3cQuWUhyjoSCWS1hlnVyWSbOfpgP0CmyceoxL4FIchTFII8TpEKQJ+MmH8UwxkeU+RPBL3siXADkKW27TKPaZDQYwA9LSpBYnZ0mS3MMRcP3HWrtCdqNNpKksdnpossKH25t0x3FkCWgykgla9z1ZhvYhkacgUhSSHLED31XRUY2dYqsQBEyeZ49iVqn5PK4IXU3lHByqNk2DcPmufVpjvtdzi3MIJs2A6dDnCnc3z3E0EoksiBOQhqVMkEYIb/2Ix//sqnKnJswmZkwGAQxtmrSMBVqasaZWYNnllqEYcqseUTVhJIhmCz1SDWFI0fw3JpJJDRqWY6kCSRNxenLzE7rzE9NcnG24KAfM1FWODdTRiFCU1W8SFBTEqz6JF//YAvJkvje7S4T9WmeWm1we39AudHm/LTE1XtHzNVsXjhlsZmWyNKc85M2Lz+3ztZRn5VJjXk7xM8kNoc+c9NzvPuoTxzD5aUKciGzYBv85KU5pquCPE95fOt1/tW/+u+4fn2PlcuXKNsZZ2oFF+amODhxWZ5o07J1SqrgyHVRZZM0TVlr1Dl2hiR5hGHqqJoMQUogQ0nTSVNBnieolk2SZZCkCCEhiidA0BM2vhBQpDFzM9O4QUDbqpJpOUdOn+/s7qGlOcMg40FngKwIfFmgqzZ+EnJ6agYLEzuPKRk6dqqQqgbDLKJhjd9Cghw5ThllKaORj6oqaLpO4kdjnl5RwY9BKOR5BKhotTJZkoMkj9kWxrkXMhmhymPDKBvX33p+zMh1qTZbJIWgyFKElNEbjogVgY7Ai2PcLCOJE3rDPotzC7x39z5emoAkI1kmcsnE1CTiJOfiQoW6KXNxwmKmZOG7EX6qoBQur5yepmLqHEcJeZE9kbCfbLUWBbk0HqkyVIa5zkgt8ejxMV1JYTDwSJKMnl+wezykainIcUFS5JRLBkYhUyvZiP/0G/+6SLOYokipyj0eDst0PImKZTJlOXQcg2q9yuPjQ1bNFLWmkwUjJtsaQRBh2A1Kasb1I58pw2Z5vs6bd/dYnZpj42TAiyuLhF4fIRlsnBxgWSa9QwejOcHNDiybPR4eGHzu5RZ/+Y3b/MynniKLAn7zBwcsTzaZKY8X5GZKBkoecasXopcmmCntsdc3eXwC93f2WZ5qM1cRTJTKfGMP9GSElyhYmoKlCmS7wE4SMj3lI8saNSWkl5aZb8r81l8e8F9/8SWWWwm7nYDAVfnGgx5ff2uTn3npaf6vGx+yXDG4ve/gKlWcY4dYLaiVa0iGwfHhHuenZ4mDcaBppAuOj4asthY4TkMS2SDojEjjeDyGZH+bCxZFRiFy1uwShSTTqjWoSjnfeLgBps4po4FeltHyjCQUZHlIu1pFpOPIpSZUrmU+l6strm4fMF3XqGj6eBPYkNgdjBctzVoVLc4YFukYJPJ9MGzwIxApSqVG4Y37IYQy/oIuYkAfM0dSrpDHY8lCkgtIsyeVcE8QYgWKMAVpnEVAtbg8Pc8o8XCyhM7xMULWyYoUSS7IChPZkMiKjHnTJCxi/t0XdC60m9zbP+CFU8vkgwF/cvOAz5+dxGg0eftGQJBr/Oob29xxIhI3+lvT9QmKLDIZimRM2mTjUc+erfGj67P0/JgsdTi5t8HyyhzvXbtLvWQRFgWjkyFGo4T8hU+vfXmtUZAqXVRZsNJSsKyEmXJOrZxTs3rU1ZCFiYLNkUxZS7m/32XrOEEWBpIqU7Nt5iwdS45JYoXp2TbLzRrpMKLeqPKDu7sszcwzcGOubQpu9Qp2+hqdvWPWJs5RK1v0Oh7nl0/jxTpv3n7Ez75wlrc+vMH378V8/pk2piF4e/uQA0cn8Ufc2E5ZX5vnrY0urz23yJ17LigW395yqRkKuSg4tVThU+cKVipHfPZSG48TrrRsVpcWmbY1LsyUuX/nEZ9/epJYxMRDhffvX+VYCILAJ49dXjmfcX7J4B9cOsWlBZvWpMH6jMX6/BR6kfDFsxVeOT3BhbbCq1dm+DszAZ9YaSMxx+vdiCTXmahUmZ9rUm5PMUgFZauEUHVQIC8EppojyTaT7QZaXlBSNO6PRpAUdIM+52bXiGU4Gg54bmUWdIOKpuPmYxryYDhksWJTFDlVsw5STiQk9ocuuapRNUs4SYpsGePxI8wpmxZxCouzU3zqk1fY3j/kQstGMmRGgxC1WkWxK+R5jiypSOPmNhCCIk6QStUx/6Oq6GWbTKRPiExA1iCNKTTB9nGHimUzimMUWSMrMp6bX2M/DiiKgsl6k/Vpi6crGVYt556josgqG7HMnz/eYdGGD7pdHh7vsjxZ556Qee/uFp6kE/vp+E0nPTkazgWyIvPFz3+WF0+1WVqokVmTXKrBck3m6v0dXKHwmStnwG6yPNdg9ewpvvXWVdZXFxmeHCO+9htfKuJMo1ayyXKfQpOpGAYltURcRNx66KKoHkpeo2Tl5KZCtbXE470hkXeCJjTarSqqAEkR5HGOLHJyuYTnuKh6k5v7I/Z7EScjjZNRQESOblbIyFip23zQ3ePFuXniKGRrr8MXnn8Wx/XZONjh45cnyUc9KvVpRqMRB4nMe5vHXH90yIUli+/eCvnSJ1YoawLDsLDjHrW2Tss2mLJU6q1Z0iIiFzGjXo8PD7d5Yf4SihjQTSO81EUWkyShSy6lrDfm+ZXf+Sbnnr7EBcvjZtfBKE1RKva5PLnGTpLxB99/zIVJhUrVRK+c4t17u4RZgpOV+ertHgkaaAoKkEpAkiCbdRRNJooiKDKkQqIIfWQtZa1usl7S+fOHB+RuxsfnpvjQc8B3eWl5nvd2tgjQGUUBrVaLmqTghS4Vvcy6aRJmgn4asuUMmC2XcLyUZr2CQ4wIYvqA5iWUm012uh00U6Np1Nh2hzRqTfb6XSzDJhj5mJqOH7gQ+WP8S1axbRvPGSBSMSbmkxhF11iaX8DIM2bKFYbiibPa6XL9YIcwBqFmFDEIYmRN5RFrnFAAACAASURBVAvnVzhfK/jYWoX5BYU/fPeIBwcehdDwSHl6McLUZtndfQwjly++8DQL1YhentHUctzIoJtDnGn82K/+Nc+fe4ZmuYoqNP7o2ru8+MqrlHyf3DKY1nwOjrts9EZ0XZUXpgqW2zrXDhLmJirYcs7+oM+puUV++62rrBNTVSTE//dr/7yYqI2dTzKXSJEo/IxyuYRZUsgknWDokisSJIKyrrN/4ICukauCWqvFb31rjy+ebhOnCVGhUatYnIxSyoqKIyyu3XjM+kobOU+RY8GdWOFwmKMVKuWZEpMCzk1VuHn3Qy48u0pnd0AnL+GGjzAUgSEsNnoJWZLy2vOzzNVinFymexLT1DTMNGSfKoY/ZKvXZ312kpKkE2UxT61PIcsFmqlxd3cbrUhol2fodk/ITcGWU/DmnSPWShGX51c4CfvMT9RIEdze8licLHH13pDFls/S1AQ9J6UfSEzZFWarEmd+7TaFaCBEQSE/adCUlSd1UgXC1ClIIZDHs3hRjFVrRSbPMhQyDEvjXEnhlZUWv/b1H4Ais9qaZLFS4/bRJgsTswT+EZ+7vMru4UMmW2W+8u4hn1k+i2bo/Pqb12nqKqfqUzxKPJ5rtrjb6eIkEZZu4YUeB0OHL156lh8cHnCcJOTIFFGMEAqFAiLJxk0/+ZM7B01AnmMZFr4/YKZpcHiUkCcRkiJRRB5FUaCVbcpGGUkFxQtBlpAVjUPPJXV6CElDziJ+8N+cpyJSDLOMVjO4dusuQqhIeYRamyArBO/tHaGlKn/1rstcy0DkEc/P2ZQFXFyZZXs4wh0qfOdxnytPXyHb2aCkWtzPNb7lG7xwapYgDmhQ8D9+7wGfO71ImoZ03SH1uMfjAKZFgGmUsDSdvlxhb+8BP/70DEWSsb8xRP6xj7/05ZplUNZgZ29I3a6g12fI4pAol0jCnFqpjqmYFDG4QUG9ZTMzPUHQ99g/GlHRFbRcYm5uGssouL2xxeWL80wvlmmZIWfONHm4v8v0dBXPktk+8ZmvQmFC24j5yJk2DwcuczM6t7dTHGGglkzu7ji8dH6J9x+cUC1pzLQssjBAFBKnGhWMNEXXdTrFEltbx3x70+Wv7/ioep2H/ZyamjE/N023d0LJbqAnDrkwscg5yhJ6RYMp08PPBFeWFhnGKbpRcOgZzKoauaTxZzcf8dR6k7nG83gnGaOojqRN8aU/2OLfXk8QqTrmHLIxuT5uucywazUEgiyKIH7yYYpAlrWxVi3JY/5ENcgymSMvIvc8nplrIlSTs60W5+dsXltqcfvY4YMjn+/dOKSX2+z442Pl3bDgGxsPUWWNcqVCe6JFtcg58hPahgKSAbLGYZyh1ye4fv8+oWFRKDqFF/8tdy8kkFUUSUGyZC7MzHHkOhBBIWX85MU5LraqPDMPDw5cylYVu6px+ewCewc+7qBHisLQczGtKqksCIOASq1BPOpy7789TU1X0XUFw1L5k7e2x51uks3Vnsejns9W18UoBMedPtVmlb2RwKjVmTcjvrbp8/5hzrZ9jgO1idFaYC8RLK612JUMbhclUiETBg5TNZv9ky4vr0yz3znh0CvGl7nNFR50ujipiS5rlHWd3UePWJ2f43dff8B3DyXu5QrivT/61eLuxgPW5xe5s7XNwkyLIgwJk4zQl9FqKjVTUOQKBx2PkqqTJSCkEOwmIono5DlhlpCGFa6cs/G9mA+2uixPlpiol/nahwEvX5plu9PlzNp5jnp90uiQsi7xznbO3m6HmpbzuY+c58OdXWYnV9kePMLUJ7n63iMCIp6an+co8PE7j5lfOs29bRe5ZNAdBCAkhhFkec4r8xXu7XRYqGX8wsurCFXhyDvisHfM6uISuiZz+/iIrd1xX9ftI4fX1qb43mFINfGYt0xeenadhw8fMPTLPHdxkg8eeNzd89lxMyLZ4o1+TJbmVDQTy7KI45hYJBx1usiFgjCscY2TUJhqlNjtHkMkj3FdDUiV8UqEKqMUCqiCLMq4sDhDcviIYZRRiIALjQZZkfL6/X0ybTwjL0zVSIuM/f4IPc9RSiaSk7DSapEVOW3bJspjHrkeoaRilFtcrJV54/4j/DSmVq4yGAzGZIuqkCOBnI1nO3l8H3C6VuGe4/KptTIXazmZZDByIuI854tnSvw/b2+yoqsUesqfbR7z8KiEqkqksoSIU4oip5AEqip4+59coGY7ZEKmJKdsDmX+j9cHTBt9rDThCx+/yHu37iKylL0wp16r0EsL3rp7wKWZOSZMmRvKGvPNBmUR82CQsdCeIMlcvCIjSAyqSs6NzpBTlkYopeRpRhGkVCaaOIM+J75E7Od4RcHPnLf5l3/wBs8stPniJ57nj28EyGbBm1dvEcUJ8suXP/Hlg77MYU+wOdIpixxdN/HTmIpps9+L6PUlpEJiYaqBpqloapksy/FlkzT3sIwq64sGDwcuR4ddPtyJmZ2c4MJCiaBI2DoYMsgVdo4CNBEwjAR3HwUM+jJX1kxOL9apGQ32ez2OhhF7SUQeaWxuHHP5qbN0/JBWY4osHPHs6fP0/ZipmsXXr+1yf5gxSsZF77JpUrMlcklCsWT+49UeiuszP2dyMMz5s7syX/v+Q3b6DdJI8OyFdZ6dXeCgl3DnyCPoDbkd2HhZk82ugqnYXN+JedjX+L2DjKGkc70f4MUpydAlNFW6wx6eG1G1LKyJNlImE2jm+ERF0hnFMkKvUFHhabuHaZZI8hhUmcmSgVG1cYYjyAXDcMjlxUUMTWJGM3h765DNkU+zUkaIca7976zM8kx1gpuHx0iKTBDGWCWTqmagqCUiW2VX5NTKDfZ7A3JF4ZE7IIrHBL7IkvGbqSgoFBmhKAgxLj1RNBVJKPSCgJ9+psWybXLQcylkmLXgC2cN1DBndW6G9bl5qumQX35tib98MCRQGlTrZZZm5ugMehTyiP/9xy8yV3ExDAkZ6PRCul6fr3znfSRrAlfofGK2zPqkiaoIJkoVzjWbPLi3wUtPvcjAPoVbmeIg14l9l26Qg+RTqer0/ARDEkxYJWKpIBEaU2UDTZXRVRk1k8hVCcOuEvX2+V/+3im+vdOlLiL+q8+9zEefWqYlO3x3P6AsFVSqJp84M438zEvPfvmvbrq4iUWaFnTclMOuYOCWuH+ccW8fHvXBj3X2hwqFrKFbIaVazupyGyFSKo2colA5PVVmcnqay8t1vn99k3OnJnh01CeVLCxVY+hrNPScii7TGTnMtkyqusWDvuDF1Sn8pMROL6BWqbDcaPLq+Rl6/U2SQiYchaBKPNjpUDdrBJmgZFjs9AMuzjeZapawFYM/ub6NkDTuHLh8cqHG+nKdW32Na7f6zE/WeHWuhVU2UITB/pbPrZMj7u4lfPLyBV6Ym+SnLrT4l1+/xjdHOt/ec3h9UPBhP6FiVhglCYET0G630Ct19LzAklOmmy0ex2UiyaK1tMxw6I8LBlUdVA1FVQgKjd//UYuPzhrMVir8/FNlVisgTvb4woqKmw74iTmFX7yscboS8J9vHLLYnuHifJN7h4f4aUGjXkEuFB6MBhx5KbIOCIUslagLwbGkszKpMaXbZMMOX3r6HC1F5/reCImcXEgkbkqhqJCNaXlJ12joOjNTk/SHPv/8oy1eWqqiFjEbXXjpdJOPtiQWWg1MQ6NVaXHvocM/fD/j4TDn8OCYT55usDJrstoucam5z6cXBM/N15ltGczYVQxdIhz4DBKVdsXGymP+y4+d4c8/7HP7MIQ8Y2qqSavaIiwq/OfoIr1Cpe/43B0K0lynI1RsVWGhNcFzTRt5dMCZmVl+sHFIRc857vSxbZ3e0CNHkFjwE5dO88HmHX7pyir/8+vHOFlBQImeVuar7z1ioxcRj3x0XWa2rIAhI37u53+pmJ5tELspAzdCFvCwn6FkMr00oef1yeKMiwtNrIrBmYmCL7zUJEvhw22PK6sy93YVzi2WEHKB42rocspEvc7WIMC2C+7vGiwvwp+/ccjC8jQi8JhowUp5gU7S4d6RRBZljLyC7X6AZSj0hj2amsnsYoteb8DNrQGlmsL5+TrN8gxfe/0WuakhkXBmqcmjrREf7IUYSsRrp5vUDIVnFw1+/7091hstJkyVu/2At7YdnpmtstoqM4pGPL2+ilJErLV0wlRlc9vjW0HBH7x1yGy5wdXOAW0lpV6eIckDtjwfxff4xEKNjzRT/qcNg4QKaClFJMZtO9n4NEYIFZIEScgICn65tcOPPl1FNS3+4/UulxZtnm6o1Kom24c+cdRB0ybYHx3x2rlV/tHvbfKr/8WLPN74kGlD5TvdPf7u4ikKITBFxtdvnnB5dor7BwGtySp+UCApsJHKvH6/Q65rfH97lx9bX+F37zxGSQVhJsjTiHKh8A+vnOV7D3a41h9RUXIWplvsdQMmKzp7A59UyEyIgp/7yBSX2xLvPuqxn9b51nHMxiCk1tT46KTM/HCfThCgqjqNiYTv3ejyLz56mu0TF8V3+ezTLXJF4l//5Rbr1ZhCslksyaw3cv7DdY+2qfD3PrLCf9pbQCiC3eERU+0ajptRsW2SDOI4ZS/NqVbLzIf7lGo2F5emeP3OEYoiURYZarlGlke4YcKFis2GkyBLMW01554HUabRMhR6SUgSRehSCakISbSM2JcpSylye/XKlyUvJrVtTpyQ3DLZHcYcjHyEWnCuWeJ/+PunWJgXOEfbnDu7Sh6pVEoSC02DsjxJf3hCEGag5AgRIJUbPD522BkJbt7vomgG377R58LZde4f5mRGieHREDcv+IurJ3zk7BkqpsXu4ZAgKnhqXuPcUokBgpNhzH4nwXck5FzisS/xYGOXgRfSbjbp+RKuXzA/MckLZ2QGPZcpQ2GuXuBHOZWpNX7rnsMdV2C0ZrkwU6Y5Ock+KlGg0EHinR2Xq49j8uY0XVHnq9cOkdMY1/H42KkltNxnsbVAKrs8PdEmUA26SPzJgcpkvcxUKWW1pbFWkpHljIKAsPCpmxJR7PHNnz/D//axGguTFfZiAxKX4/1NlhoGl6Zt/vuvXOfyygS2UQbJoCrbHPdinpspMTg8RMbi//zBJuV8jqEbILkgREKmVdk8yrjfz9lyCzpJwcNjD8fJEbKE4+XMlRv83rv3+fz6KqNRny+dX2E/iilUmdc3ttiVZV6YqbPWsNhxfNx+j5Fm4fsOXg4/cXaa7T2f/3cj5M8OY55+9jw/9+o6z01qPDVpsTI3QXu6QX1ujm/fus2V5gwzUs7H1kusLVQo/B7f3XE4Gfh0wpBypYElBWR6i1hJKdVmeObFV/nqTsazq/PcONhjtmVRVWNUq4wQgql6GbVpUVZl6k2d48MAVYGfu7LK1Ueb/NOPneH9ExfTsDBkiapWopNGnJqo4WcF2GU+PVtmP5XoBhGDMMHLoD1hsmoV/IOn1jg3WeG65yP+7a/8m2KmYvD+/jhI/+Yjh7qh0VAKXlix+JFTKrPLOl/9/g4/+YkF9Ax6lDCljDjNuLc5RCsJLE1icXqW//svbnLq1ALzM/NMSB5upjBIAjqOzNbmHh955hwffPiQ9dU2fpzweLvPZ167zM3HA75794BJJWVtdoqN/T5CV3EOu/iyihfB5EydvWGA46TMWjHXdmPkikGaFkRBQJ55vDQ5y9lZncb0LJueR4aOk0MnEkRxxi1HYhTl2JZGbxhgCJ0g6vPPLtS5Hsr84ME2FcmmU8hcqcG/uACjbJJ/8oM9Fqam2Ol2WTANptMOq+0WmQgphAVFxJHbZ8KwqOsaP/LsErO5y5nZEt1hzEbHpWW2OXGH6IbKIyfi7Y0HlAv4xRfr/PvXT3j1VJV+3mTCHXE/SHCLhIqsc7ol4QqVk1HOhClzd6Tz3pbL9Y5Lpsn0k5hPVmts5nA/SpGjmCpDvrA4R5AlFFaJP37UZV1kvLxURSgq/+Fel8zv80t//7M4wZDdOOGXL0zzK+8csVI1AZAJGEUCoWSEhQ5pgian/PTaJDePR0yVZPwoRldUDL3KncNjMk3mi8YO/747SRykLNYrhHFEpOSYmWDeNrjQsgkjh4XJMmEk8bvX92iWKvRlyDOJMEuI0hwtzymQSaWULJPpJxkNXeJ0o8Jur4OmWsgSKEKikBWcNEIpVIK8QJZlrCLBiQJIM+o1m6jICRMZXdc5dgZUdZX1skkviSBKQFYQ9//q94obWztkUcjXH8LrD/pEfsK/+9IqBh7zUzbfvrrFbH2C6bkmt/Z87m0NWJlqQRLS6zt89pU6kjC5d3CIG1g0G1M4rst6vYRUavHGvS1EplFvlHGOjtDmFvH7AdWyiWEYlHWTSwsS6TDCMGVGkYSQcnRF5e1ehlRYbPZOMCWL3/jWG2h5TkktIQuFU/WUf/b5V9g5PsQQMvcdnW54iGLXqOgKvZ7Pj5xtYxllwjCkK6k87HZ5YbKJJitsdD3ONBWCwsAsBDePTlifrNDtp/ze1ftMtxsEvSOmz57HImfZzFA1i28ddZlXdIhdCEJ+9soaslLgOEP0IkURBYpus3MUMD1jcHTYI0rLHA8GWFYJQ1OJ4oC5RoX37jyipCa8cfsxP3FxmmNfwc003j6ICQYeP//RGR6mPrOWzW+9scP3B5AJi8MsYlWTaE430Ss2jZJJVcpRhcpOGPO1713jx+eaWEXBXqFQziOaps0f3+3xb37+Yxz1OqxXDK45CSk5TzdMvn/3AQfaFGdq49LDrIBeklCr6MTukIZc5kJTxxAFkdujVW9xJFQsChbLgqsnAXvOiG23oF2uoxUBugR9UcaIXD4zb2JrEkVU4CU59/t9qDQ59hLyJMcXGanQsMkI85RfOF3jl9/aR9PLmEZOuch57dQqNx5vMmGadFWVMIzxi5TIS1AVhSwIWKo22ckTTEWjqeU87vSoNacZRT6571NkOf/4yhq/f/MxryxN8WD3iLjwETe+/oeFbQhSOcOQbX79T6+zPKfQG/l0vYALs3X2jxzOnG8Te0NuPir47PNnSPII1Ro7Sk5g8Rdv3OSZCyt0OiHbRwN+6jNP8eGjQ6KiTBy5lAybsqnxiUtnue2MODxJqJV11DxhsWUzWyljCh83ykglg4OBx+aJzygY0WrNEScOoaZzriwwvRS1apJlERP1Kt991OfgcER7osqFusTGIMGJoedEzDRtFqwCWze47Y//SeYouLg+xVdu7jDIcyxZ5WJZ4fbQ5XSrRhGnJEHOMAyZbJXRWpN8++5DdAqutA1iWePe7jGGVWK2onCpoZPHCVYa4iQwU5Y56cdUKga5mkMm2O6EJLlMVU9x+w6BUqZe16lbCkgFbsdnv9+l1J7l2AvYGkIvGfF8o8UPDhwCS+dHliepZzknaUBdifizxx7bfo4u6cimiRQ4KLIgSRJyRWK6WqHrOMSpwe9+513+8bNn+fbdIz796io/udhkIAnKhcvv3HX42dMWoS9YnFD56n7Gb795m/OLs8hZgS4rRHlMgsSkpnGmKphVfPZ9wU88s8qJXCfYfogwStw92OUg0tlMCkqSoDMMWZtqEqQxnz83jxgccjhKEVLOfQ+iLMU2LIIgYHFqkQe7Wxg4PDs/R7OkE6cp+z2fYqrFNz+8w5xmsDzRpCYJ3u0MSfKMerVBv+/QyaHIBSKHkqkQjPp89vQM04bM+ycJUZHzcOAyDH1+bGmGt44HtAyTtaaF76U4BMhf+vynvyzJguN+hK7pzJUKYgUmbcHD45xbDwcomsUHDzzmyxaKotAP4P1b29iaxnc/cNEkifnpOR4fHON6KR99/hwPHzwGqU6UxXzhlY9yfqWNptls+T5nW23cLCJBsDzZYO9kQL2kcTAQ7LgpTqFy1O1xdqbB+WmLTqgw17A56I54qlanacV05QrvbLrcP+xyabZMxdBJigxNkblzMuSVtRlQZUolm/XpCR6f+BiWgaqpnG2XefvxPma1xpSQcdOIAz9heaqELiw2ez0a5YINL8IUCvePT55cqgniIqGV+ShWjVEU8Pn1KTx3iCR0ZFVgGSaOn1Au6XiSDkXGUSfANjSIA2LFpDFh0GzUqNUsQj+gnOXIuqBWt3nc9blYLyMXMouVCnaphlwkeJFP3TKYUOHWUZfDMOcjC4tEwYjXluc48GLqhoSTj1NjswIGmoGRxPTUhMsr87w0BfpMm7PlEpqScXt7l3Jjhk+tKphywaytoWoK5ys5n54rca8veGZqml13RMNUMYTghSkLESfYmczOKOX8XIs//O6HPL2+wB/ffEymSZSrZeRUYEk51bJOHmeslEyeLqe8P5S55UQceglnp0vEuUAtClbKBe92upzTYX12mrqS0vcDKqqBZpskJx0+vTzPuRmN/V4OcsTL021yb4Rh6TwcJsiqwmQR88XFEi9VYLppsVySCQsVqSzx0uokCzNN6rKKEicslAQzDYPOSYf1ls5z55YQ3/jDPywmZBfZMAmcET943MGNDexai1OLVaZUHxQZy1BQZYvtAw9DCmlN2Nw6GOJ4KnWO+eajjB+/ss4fvXGdj12eozsquLC4RsvS2BykGJUK7nBAvWrRQ6UoCkqawXxF5rg7xNQklDDgIMrw3JgXF8oMg4woLJhqKvzu7QElqUDIUDEsZKnATyX63SNeXZ/j+x0fPUswSgYrjRqp0BkFHrlQkIoQQ9LQpZjNUcHLCy1GGSRFwKn5CXIhce3GBplaxSThoZvjZTkNEVG3Ta4f+6glHSmIeG3eomarXHvUo5/BiinRKyRO1QxKeoYbM+6Q8DJaZZkwV3jYyeh5PSpZxEKjwsxCk99+4z5VTbDvJyw2SixgMlURSLrKPSflcXfIoTA5p8rIqsnBaBfNUPnkxCS6qfBer0/iyKSmIAg8Lk20uB1l7B14ZFLKqXaNB/t9nl1osutlHI0iFusVpk0JkXSRDBU1Fiw2Ikp2Ey+JkBG4w5DNQUG1YnPixVhyRjcOUWPB2dkab2x3aJglLk23uLH9iGeWG3x7K6apZPSCnOlmFc8L2E/Biz3+7pkZ/urBPhcmDK7MtwlTja/cP0ROIEkDXl6scuzLaFLCRg/mqiZrzYIbOyMqJYMNT6ZBxrkpmzxyeW/PYXayxK6rood9tjOFj8+0eThyiVSdBSOnJYGiVzjsD5Byn1PnVpCKHETBuMy3IBNPchZ58aRiaxybFW+/9V6RdXcpJBO9XGVn4DMc+CSGTpRmNMsq1f4jVmfrRLlE4IXYho0bpIgsRTJy3nrYQdUMel2H08uzKKpKuz1FrVJhszskTHzOnz7LbFOjEIIiF+NLKzfgoHdCGMRUFJ2GoYEkIUTO0E8ZxDEzlQYd30HVVcgleuEJl0pVXMXk7skIP0uoKQrTrRLNAnZHQ2xTw1Rkto9H+JKGSUTbrtCyJR67AhKPs6dnkYBi3DZOUYx/mKIokIXChw920ZBQVZksTkgKiSSK6Xs+U80yQtLQSXHCDC/LmC/bCJHgRSH1ep0bB/usmQaKpDPIBfePB3xyrkxVSNwLAt7sJAz9mGYukaoFL9ZVDrycLjmjIOPVxVnunIx4Zlpnuxvhuh1m6hYVvYSlZaSYxHFKQYKsCm4eRjy31sYi5PpuyOpslfe3uxy5Mm0zeLJ/BZP2JEgRqqEihz2mSxLvH0aU7RqNqsH/T9m7PUuSJOd9P49LZlWdy3T3zOxiZy+4LZYACdKoZ+pVJtPfK73ITHqSmaQHSCYCJLFcgAvsYGd3bj3dfc6pqsyMCHc9eGRWnZ7ZJVFmM336dFVWZoRfP//cYzqeSLs9v3488+URvhcLH91khnDm77+ovF0Kf/S9V5yfTvyrH7xkFmE5vmG3v+X1w8T9B3vezAvHBnUpnO0AWghqPM2P/OHLF4jM3N3c84KZsyo/+OnPoBzBMiU0fvmLzxGJtAplEGya+GyqHB/P/OTjT/jhfeMXX564H2Enwpez8vHtDdP5ib0Y+3HHbDN/+rMfXY7Afu+lrIrw/CWvv/7cSinkGNDeDXX9xhAC794eef1wwj7/e17dDjwsjYfjwnKufPZw4geHkb/96ol/8Sc/Yr97weuHid3Njg8/yPz0Z39ITOJtgr/nJSIoRhAv8qkVhMRnv/6K2eD1ZKhV7mLkJy9H/v0XR7DGmEZuxCjLxMcv79mZ8FgX1IQTAuXErhXGceSbIvzbv/hD1Orvvxe7XrCGtsiyKK9/+xn7m1vyfsfXr9/w45/8kBiU0NfNhM3imPjB83//y9/C08SfvhgoKXN7f8Nff/aIxIF30xOzBJa58m//7Cd8dB/53/+/X3GwSDvPfHQ3UrTx+dT4y+/tucH4+tTY7UZ+8/UDsk98/OIDdvMDr17cMFXjGPb81S8/54NxhxwyUic+DIXD8IJ//OYNEvbcpSd22lgk8oeffJ+SBz56ccM/ffopuoBWIwwj0zLzeCrEGPnRfWSflV98LXycR357PjIEuB8zv/zmzL/62Ud88gcf+T6Kn6bqgud8xpQSqsqQhP/t//kVnxzgX/z5H9Fa29ZtXcOLrPg0kPX4592YKYti/RD5v/67r/iqHJDzxPeGiSxKzsYf/+wnfuTv71CETbb7vilG6MdMy9eff2bQe7uvztp6rhiGSmYYha//5q94PCnLuy94swhDFP703/2PvNgbSzWKCpHGYRiZW0FCRtBv3VgI63nEqxAa2uf5swmYnwLT1OnuVStNhDHs+Lt//C1zWfj+Rx/w6m7HmDORxH/8xT+iLZB2wk//5BNaLYQ4eGU2Jqzps/uxbxuJZy8z9WZ81FmrBAJ+tjKicHW/77+WWTGpNIx9zIj4wekpJcyUZt7mWc27VpMVagCxSAqBqr75Ie749X/+O9pSmEugpoG7oKSdH29b2kIKkZ/8+V9QqQgNDYG3X595evqC6UH50d2OL98cWVJkJw1NgVngX//0hxQLNAkIiskAraBBietZgiEgIfPN5+/4z7/8NYeUONxG5gp//m9/ii2VW26PDQAAIABJREFUlPyINaRh9NOmzAXtmTEUpakQu9H49no/ezPruA8ze6Ysgp9LbSFR4w1/81f/iZf3iZ/85CNfTP/U5f3d4P7XXvL1F782f7NPUqCfAbwpg36HawnCr//DX/OrTz/nv/+f/ofnAhF+t4St1/kuV/XPfYkFrB/WLddhDzgVWzri8P53iWIq28//nJeqvz/0A8NDX/hafCqJhOcL/g+fv+GQR755+8QffXLH4XDo9xi395ymGS0T+5uDGwlz2vh2uxKu9qNbS3HbZmZ8/psv+MEnP0IphKtnNVNW2fqPf/tP3KbIw7kxROVnf/kj0IAIm2W8CItsYcYaQq4eD4sI3g8uRo/Fn69Raw0R2dbm+vVMrq6EfX3G/i7oini9r+tn19+te7Gtk+F2XgS1+jtlzEy/8zu3df7ic1eIdQEum9LfusrO+h67/P47Y7BnCh6//e/hu7X0Wkh+72sVYrss+Lpx62n2/6zX9fW+62eg1srxtHCaGxaN79/f+YaIMi/KtFSmuXJ3u6fWhf04IlQ+//rI01JprfGXf/wD8hC903HdaBrzVFhOR9I4YGYcbm+eCUJrjZTSf/Ux3lckuoFb93KZK8MwdC/Hpiz+Hh8q8Gwtv+PnZ4bn2d4/31Nbh5zB7/nM736O6++9Vozf/8Hncvv7jN31d2wK1j+b9OkEQyKOo1sKXTVoFTLZNHRVlu96yPVm7GoR+G9chN91k7/rwb/rs/6Dhzb+AP985Vif06xtzwswnyfKXHn91QOH2xu+aUfCIfPuzYQeH3gqlVqEf7Bv+P5HH/DTH97yeCzc3gycFuOTH35Aa0I5TptgpZSY5hOH3Q6RW4Yxbc/dWiPGiJltyvD71mVZFn/kfhpnSsnDHLkoRc6O7EkImxW/fub3Lfq2n9+hHM/W3P/23h3Jd77v+We+/Xr/nvovL4e1X964XvD6hr9lLDdv3H///vfbd/wuEcxjv6bo1Xqr6rYJfmKQeKIShLAKLrFPPLwoERaerc/7G2kq3+0lxJ22BNmSJjP7bk/w3rXXP1tbUKvknLeztFV1E6qnqRBC4Hw+UwnegzxGRCLjmDnPlTIvPD0c+fLNkbvDniEHqI3DEHl8mglxZJlm5i8U04Ufv9oTHoyfP/o8pNOvvubh4Yk/+6NPeHE7Eizx8M2ZT999w80u8/Juz81hQJcjhjAdZ2oXoIvQ+t+vvbV7JH8ma95fFKN7HCxQSgEzQsxM58rhZqDp1TrKeh3bhGwNu5DwrRDkfQFSvQRV4X3DtcnDKmhXP/8+JegK6zl1/4wZtR/CLiKXIONaWa6M9rX8bMFYMM/RRTA1Lrd55Xmu1sT0shbpfJyQVDFZkGC8+eZISrDf7xlzIO12lFJpzRiCYEMixOw3G3qsZtdf1sDScyW4cs9wFcMH81zgmRUCuPy5PqwvNN4XoIpET8Sd/lx5+/otL+4G0jhSawXzBPpYCjd744uvH7l/dcfj2yeHcE9PlAKfNfWC2uHA6eEJKQv7JHy0g5d3kWmuxJ0w5sSf/eCGWhpfvpv54t3EN+cj37/5oR+2LidePxSKGTUJ55//ih98dM98qixl4mY38mKX2Y2R2NyiK0qLO/IQabW5QquHNjFe1k9VqW0h9mmKwSDuR0TUhS/44eUSGjklIG2x8rcMCPFi/UW70fl2yPO+8fF/cOPk29ct91Vo8r7PqEWp2ohRyB1lWj2XmYH2wWei3eC6AWvNtpzMrvR0U8Ir7/XMq1wJvXH9HJfP06MeCM+usxmJv/u//k+rwa1gxV30/S7wUIUWE2/enbnLxqv9wDAk1ATdjwzJB5kt4jcf85USbIluYAtheqom7+GvqxV85ia5xKzrdVaUodZKCIHTtBAR5trQuXLYpz5lMXKeZ2Tc8/R4ZFKlzEqiMKTEKIH7Gx9ytVRjblAU6jzz6kbYp4GUIcbMMlckGHU9V6Mabybhl4+VT7964MO7HbftzO0w8JtjoVnkB68OvHyx5+HNE19888CPXtxyuB1Zlsr+7sCHL/abhbVasAZLnYHAUpVhN7Lfjw5HqtKan3KjcyOyrpOwtIVTTZhWDrc7xp17OujJvq6KEtyLvJ97ScNsVTYjSrha73UTLh7mGgx4/z3XCe+mbB3QaAYPx5kUI7e7C/BwDXh8K2LY7lG/9fO3Ig48/HM869v5hucGa5Tha6ACkcg6ntNMHIgxQ774+b+3GCO04oWQVnmaleNJuc0eyw45kwelNshjphT3DJZ31OadVyklpCpVjBAjMXqzfUqpD8ht0JSixjw1drsdMbl71FqhFKo51JdTIh12SB5ozVAznp58IoS7dt/0eZ6JYkSBhJKGgWjC53NlWRqPj4/cxIGP7v06ZsZhn8njQCmFd3MjNOEmKTlG8q7H3n2yHrgXKs0XtdbKUipPOvIPXz/xwx++pD5OWFn8vkLiow9vqfOZ81S42488nBZqgD/48I7QN2EYU1eIBstCCOJDxaJ3xQmJ0iqlFMaYCSnz2CqtKAHl3WNjaoW//c07Xt7ueXmT+PRd5WlaCCHxzcOR/T4TKtS6MI4jSy2YNX5wf+BwkykLfPX2SBRhmX3tb1Pmgxf7nocEchJuIqT9yONpRmLgfJ5J3XAOQ2JalBgdmcrZ0a+nc2WXIiLGcVbeTTOHLLx+OPHh3S0AN+OOffDWkUMKRAmUGNBaIGTenk4gkf0AXz9MGIHdmDlNlZzg9uZAQIkSGPcDH97A3bhnHCPvjgu1KS8OCQuX6KOUwjju+fLrt7zoR/KGq5xKVZG3v/qFxZ4fLEvhPM8cnwo3u+4eTTpUGUnZuLm5AaDMC6rG3CqHYSTmSGtu0ZTkLYqqFM0QGqkPlIoiqJifdS2CmJJzprQuEOpDvCQkNGS3kNpoCFlqv8YV1CoQYyCIsJTKUvoALSAmf+AoAcEQAuMuscyNr08zZr4I37/PRIQ0Dr1GcHGlrXmCXXqb6pATkwXSYcAk8/rdI++ezgiRMs3c3bjS7gRuxsTDyVisUBZjSIGcAjkHbveZgDAEJWWva6gqinuiJP5sOSc+f1f4fz/9iqelkYaRX3w98auvHnkxBv7yox03Y+bdIvz8tw+oeAFLVbkfMvfZeJWFVzeJuxw47CIf3O2Ym/HuqbBPEKPwzdOC9kMIT8vCGJTFAstsLKXxtnnYfCrG49R4O1fOFghA0YoRiQLTNJGjn1h7mxPlXKkifHOaKLVxv9sTY+QmGprdk43RKRSqykOtRDFC2lFKoZTClAKjBWppjEm43WXymLnd7zjPHaioFbVCXSo2Dug0cbcfCW0ixszdPjPXRozGx3nHh3eZicZ8LBAD56WQU0COv/4vNrfK8d3DFt/lnKgdLgQYh4wpFPV4L4TAbjcSJaBxdTeCNgNVJHpyWGtlml3jQ3QXGZ0wgQbBmgvcfr/392sPD0zQGFG83zdJ8AfLHhtHCfjAF6VUQ7URQ/C+6g4A1Fo3wR6GgWVZaM0IAZoGHkvjeJx4uR949WroShafhXBrCFCqsiwu1C0oT1ODkPniWLjfZ4Yg1AZfvX3kVD2sezEmPrwZ+PzNmaNFfnwLN7d7avGC3NyUu/2OQRpDjtSODk1Lw1A+uh/Z7zJC4v/4D5/xv/6nz7nZjagkPjtOhKJ8eDtwvx+gFj7IGaSyywM5CCkYISfuorEfM7f7wN0ucdhn9mMkS2SujYZhFrHaKM14Ok6EPHCeFmoQHk+V2pTz0ihVySnw+VPjXTHOS+GszaHkVtHk0LE2R8oqgRQMLZUkSguBqG5optq4DcKCkqx76Bgp5t9xsxs5n2fCoLyI7q12eUQVTCvHpfDmvDDXns9Go06FVzcj+/3AF28emIrx8n7gZy8PjAk+vsscdh21a0owLwav0K6qkoo1tBrTybg5uHVsQZE8kGJlNwxIDG5pl0Ktld1uR0ieMww59ngXiCAxobV1lEqIobHb5Q2JSCl1qyuci1IUUmmEFCkVGo2UR9QiKXV0KxhjGEkRrMeUiQDicbLY4A8VdEPHTiXwq0+/5Id/8JL53SNmxrupcbeLfLDbE9R4cRM55ICWQIyBWRsxQillgz1FxEe3z0pV493DTE7GF48n0mHP64cTN/uRGDIf3R0Y93v+9tdf8qhwen0kDHvavPDzLyd+3AIhRko1Wm081hkpBTNX5L/51Wf8yx9/zE02Xt7tOE7Gl28f+c2biX/50R2nZgwEfrjbsYhzwlII1JBpQJaEGMzmhabBIhZACaQ0+MmyIREIpCF15RfOCpoTaWl8tE+MIZHSDiTwy88fKQ1ux8huiLx5d+LVQXlXGl8eA18fK0cD0cxdTCytsstpo8fcDZGbqOwN4m5ASmEYEve7hLaFu9sd0Zw8odWIOfH1u6OfPyE3PtkwwZiHHqrDbvAEPY0D7x4X/vH1I6+fKqcs/MUfvOI8veM+3/NiFH788o6lFV7djrx6Mbqnb57ojzlQTTkdC2nNoR4++wcDZToVzqeZZsphiB7GlILRGHaHLSER8X9LKXSLbR0mXaG7QGkNK8qyLARJPgy4J3ir9RYRgiSmUplnoe78ALzaHCkJoSfWIaE0Uhq4ZULicyjS1LH3poXTDMGUp1Plt69PTAW0J/FiRs6RapWXhwM//nBPFWMAzs0oS8NSIPdKfe6hU2uN06wcTwvTXDkWZS6FRYUlZl7cJl7c7hjNHEIoxlfTBKUxIbQmLE0JGGeD2xR5nCqLGZQCWTgkn9X017/9nH/zve+Rc+NFiuRx4It3T+zHzNgRteOy0BSaORVhbo1diEjIqFViMCKRHAO7JOwH4W7n8f5hjMQYnqM+pj3fC+ySV3BjcDRQxHj96OuXY2LWSqsKDR6miXfnwjdPMEojJuG4GFMtqPq0nV0IjAObwh+ieXgblYRxuxuYzYgqNIzTuXUP6WOuKhd49zBEYhRqhRkl9aFqtfopS0OH2XdDcm+EccgRFWWXAvf7wDC6N4kKiyq5F6JbjzqqNNJqsXNWas5Ok6UfA0ugaWBZKjlHymLEpEBDxAjpgjpMk0OJQRJtUY7nM2VpjDujmdc4hmGgLQshuPUtbaKUxuup0t4Z4XDryXZOiMC8LAiNtw8Tt7cwJGEIz4tITY1//PzIm7dHijY+vLvlzenM46lxe5u4DZlvnnyO6NIWcsj86vO3HHaRV7c7/v63b0nRWa0pB1pcizptC59S6qxXUwaDqh74iQivhsAPbxOffX1mPyZefjBwOAQ+ez1xF4Uv3y2cWwELPBbltSy0ydAUyBIIGmBpDMH4777/iptYuBkyaQzsozHlSBTh5WHH23niZpfJKbHMFROYWwQCWZRaArsxMgxwOwQO+8whRYYxICo0iYRgWw1jiIEgXqwL4sfS+rTjTAgGQchpYrGIxcg0zZTSsHWsvhoxNJ5aoJ4nctixy4I2eDVG7u+yvy8EICBifj6LFt49Vn71zYmpGovCUzG+ejqzVKGIQQsULSzF59d24eIuJCw0zvNMDglLgVgLH+4HhhQ57EY+HDMpK5+8yry62UNrFBF0McbBhzOPQ0INSmsomalWoiTk6be/MjO3kFrXarPHhUBnIyq7cSTGyPHpzKLKkBK3tzcMY0/EjlPH0w2txqKKFmVaPEleiiIpsM+BfU6kHHg8Vd4+TjxMBYB3c6FU5XRekCTsx8Hd2ziwD4Gqwr/7i++Tx0hAqKXQqvLzT9/w8n7km4eFuSkPp2UrJP3Z91+hUnn38MSHL255/XDm129mrBb+8AcfcjxN5JwZI+w6+jMOHoIlgZiEqk7ROC2VdyevNZyLAw4xwMsxQhC+/+qOD3bC43nm3bHSEP7uyyNvZodXm3l+FaLycszcj4Ka8fE+c5vcEuaUSAmGFBmSI3pN/QTZ42lmas4/Kk1pVRmHiJlj7SZGIiChMY6ZmAK7mJHI5vEQt7QxujEbcuThuGAoTR28rK3wwe0NQYRpLrx+WjifJ8yUXYqkKAypRwJmBEvMraLdou93gfvdnmBQxahLYfGT7dxDdHrlfJ6QkPjm2Hh9LnzxuPB2KpwKTLXwdpr41x/doQJtWhj3B8q8MKSAaINgvCsVq0KthY9vR/YDjGrsc6aJEEJlTJFmkSHBLhpT7WAJkWqKBuH+sOPuMCCnL/7JvMoZHPfWS+ncQyCvHLd+wMbcE6ZhGHj1ws9LKJ3cFmPAglGL9QJL4+mk1NpYqnpsy6X2EIK7X1VYuvurTVma0qpReh0lRg/PllKppvybP/4Rr26Mt08LZaqUVtEQefu4sBRlsUZZGq2TvIIFPn6154+/d8vTVPmnrx6ZSuWbEyQpBBm422Ve3kSqwS4n9ikQBmOfBuZSONdGacJxco/3+rigTXh1mxiye9kff3zDUhqteOEwSuLTr5/4+ZszpRmv8p7DqNzsE7dBaB2g+JOPvKnfDznx+71UqT3hi33s5Ky+Ns2U1jwZtZ4QgpFSdiYpDZWEBBhDYsxCCYEhCFHcajeM3Cv2c3NBmWbtrQBGCMI4DBRt1GJM5zMhmHs2ceWqVgkWKa2RgvW8cS2QNS8YagESkgSztZDWEBWqKcc58O408XhqnGplagVtPoRNUD682QFwLMqQAods7GMErYQAv3nzxDLDn3z/lptDIDSjVSFIo6p1Cro4ozhcFfNomApnFYYhEcWQx88+7VQR1+6lNqI8J3KV5olIa7ZBminB/rAjxkidC1WddZuHdKk6NmOaKudqnOfZwymMXXI05+4g1KqUxZibUlpjLs0RjUWp6la1mdDMESV3sUai8ep2oDTPS5ZWSRI8GW4uRFU7ZIpymxI//dELornr/YffPvLFqRJsRshUhRyUwziAGneHzAeHgf0uMUZhUeU8KW9Old0QeFgaEPj4dqSUmd2QeTpXTsXY76KHEzGSE8Q08OWbRz55ted2GFAJnOaJHBOn6cz37gYWFVKGZLKtMfiahuA2VU1IUSlVqObwbFUlBAcrAl7HyCkQcURNUGRIGInFIGPIWv5tSgyeU6y1kKdZKKaEnKgqyM4T9aCGNuN4PGOlEwSTw+LkDGpM04QahFY8fxgzSTzeD5Jo0i6MYVNqAXLk4VgxDXz1OPGbx6O3IVgjSuDN7NBrioMfPqMKChKFuxg4BOFcF5oF7sfEq9vEPBeHrAV22QGTuyxIDExlIcZIMCh4OQGUmLyWIk+/+Sdbq59u1RWJcVOQFQ5t1dvv6BBnSh6HTnOn+6bIkCNDn/1rvULYmqIEllqYingxauUZhQbmrvvUYb1SG0vz6ilAitKrqW6hV/ZtMbdUpRRHrlSZqoAa+9HduvT7Py8LFiJDEP7g5YF9zPzD6wffnF6cMXWli10AxyFyMw7c7IQUIqrw7jTxdFYaQlG/X6cXeFJI80RQMYoZUV1Zx11AtHPGoh+lFmNgJ8KYhRQyhyFhohu9wR16pC6FcUiMIbDfKYsmjufZXT0OIVZVltnv/cPbxG4YPP4nUHOgVOOr48xkjSH650/TTDbhxS7x8e2IdATwHAPNhFIaiit1Su6tytQ4nU48PU0sxe910YZI9oNQcA9g1e/5g/s9+xzJnRltOVPLjBZv0ZUamIOxzIXHeUZL5Nw6Ukhi0srSAkEaVo2zKqlTRgYVzuXM/ZiISRglIhGHn6kMOTCXgtVAjsIuBcYRV3yDXcpM1QcPZHG5agbyxS8/tdWNLaWx6MIQBlL2EfnaAktr3O9j5w+tliswlcvJkMKlP2BlU9WOyXs+4gW5YBdahmPgPSmvhWrBlUkUPwskQG/K2eLfABHZDo4RET8yWBsPTzNTEaI4omQ0sMDxXIg5EIgM0S1nQqgYtUCxtbjvniX1msna4bV+jzZxlKnWLgxOTIvqFXo1oagxNaO0yk0HHV7uM1mEqRlYI+dMK4UhebI51UqCLfRZkbRoUDFa6xwgcwJgiIb1ZiyjopYQbQSDlL2I6efEwXHR7tEDWj3uvh1HRGwLmZS2ccRyCiSBou2y7n2/c8Q9TxK0BUwr4xhJkkjdy5RWOZ8WcvZCWM7CYUxoPwHr6dyYqzAXB2EO2XtwUogsRTk3eJjVQ3ETxiFSSuHxrBznQghCrZ2ha4FilZgV+jDzTz644XR64gcvblA1Tkvhy7cn7seBTz7c9+Kn8DAtHBfz001F+PTtIzf7A/Kb//KPNi3Kw+OJ+w/8SF5desNLNlLHts0azfRCxLNARYhIrwX4wsfUj1jCvZsLvdDqpdDlyuboRuwI1LwoZVGWVhlSwMQx9maGrg04SUhBtta/tXCoGKoeStWiW1tiEseZV++3NTsF824wM4IYtdnWPmtmW7zuinsRiGaKKszFPVjVlRXs/BjBQPEjp8QT/1WxEA/5ak9EV9pAELdY0q2oAtbp34iHSa2y8YrW61WMpfjaVoxkyi55y2UKHjaiTquoemEQ75LzidaCZ+rJ9Vq/UTOIbpik/z6udagoHo7J2olm5CGSg3datlVQI57DNWWfPamvCg/nxsNsDNE2HpH2nDULnNRBroYhajSEViov7wZEfeDEz7888/nTGWuN791khix8uB8ZkhceQ8wYjd3olIylVL765swwRlIQ9mPkPLuROQweuczFsBgIaqSvvjlynCuHfXZeSAg84QlSDoKq0dQttirUjZnqC+j22z2FqhJzIqW+MAi2Fuhir1AriBopGiJK6Jj3sE+cRLEiIMZ+8I2yasjgljeooy+rUITUmZvW1aIqxWBaqucSAWLqRLcYOdeF3RC9BRRv45XgBDlZWzt7F9lK9tKuGIgwSO6YurfLmgpVnJNkQShV0apgEKLXZQSIOfk9DMYo4yZ81omR+UoIE0a94o2aeZ51TYrUECnzwj5DNGUcUve8fd1FkCigfkRA0avknAu1PKfOHDbc8PSW1bXaH0OnZYu4txZhFCHkTp8JTviTYP7cAVp1V5BzZhh6GF69Ml/Mz6fLObPPO7DG41SJCIs2orgS3g3OG8shoirkaMQotKJ88mLgg33gfp+IGDdDYlpmYgzc3iZyDtASzYwhZXY7uL+5ce8Xve5Q1mKoQNUdc3FjD5BePx7ZjXvGlDeik4cjSpmlu1NP9NzK+mYWgfPsLM229LpECMBCNWWIiaWTBfc5kHKn/bYG4lXSPERMhSBGSH7tFAK1NkrxjSv6XrNIt6ZLpwmbOaJgArUpU60Ug6UolgNSjSEEqroVaBaw4HG+0dmQ0Q8n99AP76Xo3xdxj0IM0Fw4QhCGwavmWRRLnn9IGGhRUZRdTsxLo3biWBoj2cQTuTVfU4gCeS02Om2MGK2DAm7hHfqNqDrPZwewHxiy37OzcQNo2xoz/QkCtTWGDvmuYZf0mFl6YSp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   "source": [
    "d2l.set_figsize()\n",
    "content_img = image.imread('../img/rainier.jpg')\n",
    "d2l.plt.imshow(content_img.asnumpy());"
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   ],
   "source": [
    "style_img = image.imread('../img/autumn_oak.jpg')\n",
    "d2l.plt.imshow(style_img.asnumpy());"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 预处理和后处理图像\n",
    "\n",
    "下面定义图像的预处理函数和后处理函数。预处理函数`preprocess`对输入图像在RGB三个通道分别做标准化，并将结果变换成卷积神经网络接受的输入格式。后处理函数`postprocess`则将输出图像中的像素值还原回标准化之前的值。由于图像打印函数要求每个像素的浮点数值在0到1之间，我们使用`clip`函数对小于0和大于1的值分别取0和1。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "4"
    }
   },
   "outputs": [],
   "source": [
    "rgb_mean = nd.array([0.485, 0.456, 0.406])\n",
    "rgb_std = nd.array([0.229, 0.224, 0.225])\n",
    "\n",
    "def preprocess(img, image_shape):\n",
    "    img = image.imresize(img, *image_shape)\n",
    "    img = (img.astype('float32') / 255 - rgb_mean) / rgb_std\n",
    "    return img.transpose((2, 0, 1)).expand_dims(axis=0)\n",
    "\n",
    "def postprocess(img):\n",
    "    img = img[0].as_in_context(rgb_std.context)\n",
    "    return (img.transpose((1, 2, 0)) * rgb_std + rgb_mean).clip(0, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 抽取特征\n",
    "\n",
    "我们使用基于ImageNet数据集预训练的VGG-19模型来抽取图像特征 [1]。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "5"
    }
   },
   "outputs": [],
   "source": [
    "pretrained_net = model_zoo.vision.vgg19(pretrained=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了抽取图像的内容特征和样式特征，我们可以选择VGG网络中某些层的输出。一般来说，越靠近输入层的输出越容易抽取图像的细节信息，反之则越容易抽取图像的全局信息。为了避免合成图像过多保留内容图像的细节，我们选择VGG较靠近输出的层，也称内容层，来输出图像的内容特征。我们还从VGG中选择不同层的输出来匹配局部和全局的样式，这些层也叫样式层。在[“使用重复元素的网络（VGG）”](../chapter_convolutional-neural-networks/vgg.ipynb)一节中我们曾介绍过，VGG网络使用了5个卷积块。实验中，我们选择第四卷积块的最后一个卷积层作为内容层，以及每个卷积块的第一个卷积层作为样式层。这些层的索引可以通过打印`pretrained_net`实例来获取。"
   ]
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  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "6"
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   },
   "outputs": [],
   "source": [
    "style_layers, content_layers = [0, 5, 10, 19, 28], [25]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在抽取特征时，我们只需要用到VGG从输入层到最靠近输出层的内容层或样式层之间的所有层。下面构建一个新的网络`net`，它只保留需要用到的VGG的所有层。我们将使用`net`来抽取特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "7"
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   },
   "outputs": [],
   "source": [
    "net = nn.Sequential()\n",
    "for i in range(max(content_layers + style_layers) + 1):\n",
    "    net.add(pretrained_net.features[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "给定输入`X`，如果简单调用前向计算`net(X)`，只能获得最后一层的输出。由于我们还需要中间层的输出，因此这里我们逐层计算，并保留内容层和样式层的输出。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "8"
    }
   },
   "outputs": [],
   "source": [
    "def extract_features(X, content_layers, style_layers):\n",
    "    contents = []\n",
    "    styles = []\n",
    "    for i in range(len(net)):\n",
    "        X = net[i](X)\n",
    "        if i in style_layers:\n",
    "            styles.append(X)\n",
    "        if i in content_layers:\n",
    "            contents.append(X)\n",
    "    return contents, styles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面定义两个函数，其中`get_contents`函数对内容图像抽取内容特征，而`get_styles`函数则对样式图像抽取样式特征。因为在训练时无须改变预训练的VGG的模型参数，所以我们可以在训练开始之前就提取出内容图像的内容特征，以及样式图像的样式特征。由于合成图像是样式迁移所需迭代的模型参数，我们只能在训练过程中通过调用`extract_features`函数来抽取合成图像的内容特征和样式特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "9"
    }
   },
   "outputs": [],
   "source": [
    "def get_contents(image_shape, ctx):\n",
    "    content_X = preprocess(content_img, image_shape).copyto(ctx)\n",
    "    contents_Y, _ = extract_features(content_X, content_layers, style_layers)\n",
    "    return content_X, contents_Y\n",
    "\n",
    "def get_styles(image_shape, ctx):\n",
    "    style_X = preprocess(style_img, image_shape).copyto(ctx)\n",
    "    _, styles_Y = extract_features(style_X, content_layers, style_layers)\n",
    "    return style_X, styles_Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义损失函数\n",
    "\n",
    "下面我们来描述样式迁移的损失函数。它由内容损失、样式损失和总变差损失3部分组成。\n",
    "\n",
    "### 内容损失\n",
    "\n",
    "与线性回归中的损失函数类似，内容损失通过平方误差函数衡量合成图像与内容图像在内容特征上的差异。平方误差函数的两个输入均为`extract_features`函数计算所得到的内容层的输出。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "10"
    }
   },
   "outputs": [],
   "source": [
    "def content_loss(Y_hat, Y):\n",
    "    return (Y_hat - Y).square().mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 样式损失\n",
    "\n",
    "样式损失也一样通过平方误差函数衡量合成图像与样式图像在样式上的差异。为了表达样式层输出的样式，我们先通过`extract_features`函数计算样式层的输出。假设该输出的样本数为1，通道数为$c$，高和宽分别为$h$和$w$，我们可以把输出变换成$c$行$hw$列的矩阵$\\boldsymbol{X}$。矩阵$\\boldsymbol{X}$可以看作由$c$个长度为$hw$的向量$\\boldsymbol{x}_1, \\ldots, \\boldsymbol{x}_c$组成的。其中向量$\\boldsymbol{x}_i$代表了通道$i$上的样式特征。这些向量的格拉姆矩阵（Gram matrix）$\\boldsymbol{X}\\boldsymbol{X}^\\top \\in \\mathbb{R}^{c \\times c}$中$i$行$j$列的元素$x_{ij}$即向量$\\boldsymbol{x}_i$与$\\boldsymbol{x}_j$的内积，它表达了通道$i$和通道$j$上样式特征的相关性。我们用这样的格拉姆矩阵表达样式层输出的样式。需要注意的是，当$hw$的值较大时，格拉姆矩阵中的元素容易出现较大的值。此外，格拉姆矩阵的高和宽皆为通道数$c$。为了让样式损失不受这些值的大小影响，下面定义的`gram`函数将格拉姆矩阵除以了矩阵中元素的个数，即$chw$。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "11"
    }
   },
   "outputs": [],
   "source": [
    "def gram(X):\n",
    "    num_channels, n = X.shape[1], X.size // X.shape[1]\n",
    "    X = X.reshape((num_channels, n))\n",
    "    return nd.dot(X, X.T) / (num_channels * n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "自然地，样式损失的平方误差函数的两个格拉姆矩阵输入分别基于合成图像与样式图像的样式层输出。这里假设基于样式图像的格拉姆矩阵`gram_Y`已经预先计算好了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "12"
    }
   },
   "outputs": [],
   "source": [
    "def style_loss(Y_hat, gram_Y):\n",
    "    return (gram(Y_hat) - gram_Y).square().mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 总变差损失\n",
    "\n",
    "有时候，我们学到的合成图像里面有大量高频噪点，即有特别亮或者特别暗的颗粒像素。一种常用的降噪方法是总变差降噪（total variation denoising）。假设$x_{i,j}$表示坐标为$(i,j)$的像素值，降低总变差损失\n",
    "\n",
    "$$\\sum_{i,j} \\left|x_{i,j} - x_{i+1,j}\\right| + \\left|x_{i,j} - x_{i,j+1}\\right|$$\n",
    "\n",
    "能够尽可能使邻近的像素值相似。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "13"
    }
   },
   "outputs": [],
   "source": [
    "def tv_loss(Y_hat):\n",
    "    return 0.5 * ((Y_hat[:, :, 1:, :] - Y_hat[:, :, :-1, :]).abs().mean() +\n",
    "                  (Y_hat[:, :, :, 1:] - Y_hat[:, :, :, :-1]).abs().mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 损失函数\n",
    "\n",
    "样式迁移的损失函数即内容损失、样式损失和总变差损失的加权和。通过调节这些权值超参数，我们可以权衡合成图像在保留内容、迁移样式以及降噪三方面的相对重要性。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "14"
    }
   },
   "outputs": [],
   "source": [
    "content_weight, style_weight, tv_weight = 1, 1e3, 10\n",
    "\n",
    "def compute_loss(X, contents_Y_hat, styles_Y_hat, contents_Y, styles_Y_gram):\n",
    "    # 分别计算内容损失、样式损失和总变差损失\n",
    "    contents_l = [content_loss(Y_hat, Y) * content_weight for Y_hat, Y in zip(\n",
    "        contents_Y_hat, contents_Y)]\n",
    "    styles_l = [style_loss(Y_hat, Y) * style_weight for Y_hat, Y in zip(\n",
    "        styles_Y_hat, styles_Y_gram)]\n",
    "    tv_l = tv_loss(X) * tv_weight\n",
    "    # 对所有损失求和\n",
    "    l = nd.add_n(*styles_l) + nd.add_n(*contents_l) + tv_l\n",
    "    return contents_l, styles_l, tv_l, l"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 创建和初始化合成图像\n",
    "\n",
    "在样式迁移中，合成图像是唯一需要更新的变量。因此，我们可以定义一个简单的模型`GeneratedImage`，并将合成图像视为模型参数。模型的前向计算只需返回模型参数即可。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "15"
    }
   },
   "outputs": [],
   "source": [
    "class GeneratedImage(nn.Block):\n",
    "    def __init__(self, img_shape, **kwargs):\n",
    "        super(GeneratedImage, self).__init__(**kwargs)\n",
    "        self.weight = self.params.get('weight', shape=img_shape)\n",
    "\n",
    "    def forward(self):\n",
    "        return self.weight.data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面，我们定义`get_inits`函数。该函数创建了合成图像的模型实例，并将其初始化为图像`X`。样式图像在各个样式层的格拉姆矩阵`styles_Y_gram`将在训练前预先计算好。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "16"
    }
   },
   "outputs": [],
   "source": [
    "def get_inits(X, ctx, lr, styles_Y):\n",
    "    gen_img = GeneratedImage(X.shape)\n",
    "    gen_img.initialize(init.Constant(X), ctx=ctx, force_reinit=True)\n",
    "    trainer = gluon.Trainer(gen_img.collect_params(), 'adam',\n",
    "                            {'learning_rate': lr})\n",
    "    styles_Y_gram = [gram(Y) for Y in styles_Y]\n",
    "    return gen_img(), styles_Y_gram, trainer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练模型\n",
    "\n",
    "在训练模型时，我们不断抽取合成图像的内容特征和样式特征，并计算损失函数。回忆[“异步计算”](../chapter_computational-performance/async-computation.ipynb)一节中有关用同步函数让前端等待计算结果的讨论。由于我们每隔50个迭代周期才调用同步函数`asscalar`，很容易造成内存占用过高，因此我们在每个迭代周期都调用一次同步函数`waitall`。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "17"
    }
   },
   "outputs": [],
   "source": [
    "def train(X, contents_Y, styles_Y, ctx, lr, max_epochs, lr_decay_epoch):\n",
    "    X, styles_Y_gram, trainer = get_inits(X, ctx, lr, styles_Y)\n",
    "    for i in range(max_epochs):\n",
    "        start = time.time()\n",
    "        with autograd.record():\n",
    "            contents_Y_hat, styles_Y_hat = extract_features(\n",
    "                X, content_layers, style_layers)\n",
    "            contents_l, styles_l, tv_l, l = compute_loss(\n",
    "                X, contents_Y_hat, styles_Y_hat, contents_Y, styles_Y_gram)\n",
    "        l.backward()\n",
    "        trainer.step(1)\n",
    "        nd.waitall()\n",
    "        if i % 50 == 0 and i != 0:\n",
    "            print('epoch %3d, content loss %.2f, style loss %.2f, '\n",
    "                  'TV loss %.2f, %.2f sec'\n",
    "                  % (i, nd.add_n(*contents_l).asscalar(),\n",
    "                     nd.add_n(*styles_l).asscalar(), tv_l.asscalar(),\n",
    "                     time.time() - start))\n",
    "        if i % lr_decay_epoch == 0 and i != 0:\n",
    "            trainer.set_learning_rate(trainer.learning_rate * 0.1)\n",
    "            print('change lr to %.1e' % trainer.learning_rate)\n",
    "    return X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面我们开始训练模型。首先将内容图像和样式图像的高和宽分别调整为150和225像素。合成图像将由内容图像来初始化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "18"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  50, content loss 10.09, style loss 29.40, TV loss 3.46, 0.01 sec\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 100, content loss 7.50, style loss 15.46, TV loss 3.89, 0.01 sec\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 150, content loss 6.30, style loss 10.37, TV loss 4.15, 0.01 sec\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 200, content loss 5.63, style loss 8.11, TV loss 4.29, 0.01 sec\n",
      "change lr to 1.0e-03\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 250, content loss 5.57, style loss 7.93, TV loss 4.30, 0.01 sec\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 300, content loss 5.51, style loss 7.78, TV loss 4.31, 0.01 sec\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 350, content loss 5.45, style loss 7.63, TV loss 4.31, 0.01 sec\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 400, content loss 5.39, style loss 7.48, TV loss 4.32, 0.01 sec\n",
      "change lr to 1.0e-04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 450, content loss 5.38, style loss 7.47, TV loss 4.32, 0.01 sec\n"
     ]
    }
   ],
   "source": [
    "ctx, image_shape = d2l.try_gpu(), (225, 150)\n",
    "net.collect_params().reset_ctx(ctx)\n",
    "content_X, contents_Y = get_contents(image_shape, ctx)\n",
    "_, styles_Y = get_styles(image_shape, ctx)\n",
    "output = train(content_X, contents_Y, styles_Y, ctx, 0.01, 500, 200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面我们将训练好的合成图像保存起来。可以看到图9.14中的合成图像保留了内容图像的风景和物体，并同时迁移了样式图像的色彩。因为图像尺寸较小，所以细节上依然比较模糊。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "19"
    }
   },
   "outputs": [],
   "source": [
    "d2l.plt.imsave('../img/neural-style-1.png', postprocess(output).asnumpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![$150 \\times 225$尺寸的合成图像](../img/neural-style-1.png)\n",
    "\n",
    "为了得到更加清晰的合成图像，下面我们在更大的$300 \\times 450$尺寸上训练。我们将图9.14的高和宽放大2倍，以初始化更大尺寸的合成图像。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "20"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch  50, content loss 13.78, style loss 13.72, TV loss 2.37, 0.02 sec\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 100, content loss 9.50, style loss 8.76, TV loss 2.65, 0.02 sec\n",
      "change lr to 1.0e-03\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 150, content loss 9.20, style loss 8.43, TV loss 2.67, 0.02 sec\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 200, content loss 8.93, style loss 8.15, TV loss 2.69, 0.02 sec\n",
      "change lr to 1.0e-04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 250, content loss 8.90, style loss 8.12, TV loss 2.70, 0.02 sec\n"
     ]
    }
   ],
   "source": [
    "image_shape = (450, 300)\n",
    "_, content_Y = get_contents(image_shape, ctx)\n",
    "_, style_Y = get_styles(image_shape, ctx)\n",
    "X = preprocess(postprocess(output) * 255, image_shape)\n",
    "output = train(X, content_Y, style_Y, ctx, 0.01, 300, 100)\n",
    "d2l.plt.imsave('../img/neural-style-2.png', postprocess(output).asnumpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到，由于图像尺寸更大，每一次迭代需要花费更多的时间。从训练得到的图9.15中可以看到，此时的合成图像因为尺寸更大，所以保留了更多的细节。合成图像里面不仅有大块的类似样式图像的油画色彩块，色彩块中甚至出现了细微的纹理。\n",
    "\n",
    "![$300 \\times 450$尺寸的合成图像](../img/neural-style-2.png)\n",
    "\n",
    "\n",
    "## 小结\n",
    "\n",
    "* 样式迁移常用的损失函数由3部分组成：内容损失使合成图像与内容图像在内容特征上接近，样式损失令合成图像与样式图像在样式特征上接近，而总变差损失则有助于减少合成图像中的噪点。\n",
    "* 可以通过预训练的卷积神经网络来抽取图像的特征，并通过最小化损失函数来不断更新合成图像。\n",
    "* 用格拉姆矩阵表达样式层输出的样式。\n",
    "\n",
    "\n",
    "## 练习\n",
    "\n",
    "* 选择不同的内容和样式层，输出有什么变化？\n",
    "* 调整损失函数中的权值超参数，输出是否保留更多内容或减少更多噪点？\n",
    "* 替换实验中的内容图像和样式图像，你能创作出更有趣的合成图像吗？\n",
    "\n",
    "\n",
    "\n",
    "## 参考文献\n",
    "\n",
    "[1] Gatys, L. A., Ecker, A. S., & Bethge, M. (2016). Image style transfer using convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2414-2423).\n",
    "\n",
    "## 扫码直达[讨论区](https://discuss.gluon.ai/t/topic/3273)\n",
    "\n",
    "![](../img/qr_neural-style.svg)"
   ]
  }
 ],
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